System

Abstract

A system is provided.SOLUTION: A system for preventing a specific individual from abusing his / her face photograph, comprising: means for registering a face photograph; means for storing the registered face photograph in a database; means for collating the face photograph with the database at the time of an image generation request; means for permitting or rejecting image generation based on a collation result; and means for collating the generated image with the database again.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, there has been an increase in cases where generative AI technology has been misused to generate obscene images based on photographs of other people's faces and to blackmail them. This situation significantly violates individuals' privacy and reputation, causing social problems. The purpose of this invention is to provide a mechanism for specific individuals to prevent their own facial photographs from being misused by generative AI and to check whether a specific face is included in the generated images. [Means for solving the problem]

[0005] The present invention is a system for preventing a specific individual from misusing his or her facial photograph, and includes the following means.

[0006] First, there is a means to register a facial photograph: users upload a photograph of their face to the system via a dedicated app or website and register it along with their identification information.

[0007] Second, it provides a means to store the registered facial photograph in a database, which then serves as a reference for later matching.

[0008] Third, when an image generation request is made, the AI ​​compares the facial photo data included in the request with the database to see if it matches the blacklist.

[0009] Fourth, there is a means for permitting or denying image generation based on the result of the match: if the match result matches the blacklist, the image generation is denied and a notification is sent to the user or a third party.

[0010] The fifth step is to check the generated image against the database again. The generated image is also checked against the database to see if it contains the face of a specific person. If a match is found, the image is invalidated and the user is notified.

[0011] Combining these measures can prevent individuals' facial images from being misused by generative AI, ensuring privacy and safety.

[0012] A "face photo" is image data of an individual's face, and is used as identification information.

[0013] A "database" is a collection of information that is organized, stored, and managed so that it can be searched and referenced later.

[0014] "Generative AI" refers to systems or programs that use artificial intelligence technology to generate new images and data.

[0015] A "blacklist" is a registration list that restricts access or use based on certain conditions, and in this case refers to a list that includes specific facial photographs to prevent abuse.

[0016] "Matching" is the process of comparing specific data or information with an existing database to identify matches or discrepancies.

[0017] "User" refers to an individual or third party who uses the system, and in this case includes a person who registers a photo of their face and a person who uses generation AI to generate an image.

[0018] A "facial recognition algorithm" is a computational method or process for analyzing facial features in an image and performing authentication or identification based on that analysis.

[0019] An "image generation request" is a request from a user or third party to a generation AI to generate an image. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] This invention relates to a system that prevents specific individuals from misusing their facial photos in AI-generated images. Below, we will explain the specific processing of the system's program and provide details with concrete examples.

[0042] 1. Face photo registration process

[0043] User

[0044] To register their own face photo in the system, users access a dedicated application or website, upload their own face photo, and enter their personal information (name, contact details, etc.).

[0045] Terminal

[0046] The device will temporarily store the uploaded facial photo and use a facial recognition algorithm to verify whether the photo is appropriate. The facial recognition algorithm will analyze the facial features of the uploaded photo to ensure it is a valid facial photo.

[0047] server

[0048] The server stores the user's confirmed facial photo and personal information in a database. After registration is complete, the server notifies the device, and the device displays a message to the user indicating that registration is complete.

[0049] 2. Generate Request Validation Process

[0050] Terminal

[0051] The device receives an image generation request from a user or a third party, temporarily stores the facial photo data included in the request, and then sends a matching request to the server.

[0052] server

[0053] The server receives the matching request and matches the face photo in the request with the face photos stored in its database, using a facial recognition algorithm to compare facial feature points and see if there is a match.

[0054] If the server finds a face photo that matches the blacklist, it notifies the device of the result. If there is no match, it returns a "no match" status to the device.

[0055] 3. Image generation permission / denial process

[0056] Terminal

[0057] The device receives a notification from the server and stops image generation if a face matching the blacklist is found, notifies the user or a third party of the reason for the stop, and terminates the generation process.

[0058] If a "no match" notification is received from the server, the device proceeds with the image generation process.

[0059] 4. Face recognition process after generation

[0060] Terminal

[0061] Once the generated image is complete, the terminal sends it back to the server for verification.

[0062] server

[0063] The server checks the generated image against a database to see if it contains a specific person's face, and if a match is found, invalidates the image and notifies the user of the reason.

[0064] If no match is found, the server notifies the terminal of "no match" and the terminal returns the generated image to the user.

[0065] Specific examples

[0066] 1. Register your face photo

[0067] User "X" uploads a photo of their face via a dedicated app and enters the necessary personal information. The device uses a facial recognition algorithm to verify that the photo is valid, and the server stores it in a database.

[0068] 2. Validating the Generated Request

[0069] A third party "Y" attempts to generate an obscene image based on a specific facial photograph. The device that receives the request sends a matching request to the server, which checks the database and confirms that the facial photograph matches that of user "X." The device rejects the image generation and notifies third party "Y."

[0070] 3. Face recognition after generation

[0071] A third party, "Z," uses a generation AI to generate an image and sends it to the server. The server checks it against a database, finds no match, sends a "no match" notification to the device, and returns the image to the third party, "Z."

[0072] In this way, the present invention effectively prevents specific individuals from being misused by the generation AI, thereby protecting privacy and preventing fraudulent activities.

[0073] The processing flow will be explained below.

[0074] 1. Face photo registration process

[0075] Step 1:

[0076] User

[0077] Users access a dedicated app or website and begin the process of registering their face photo.

[0078] Step 2:

[0079] User

[0080] Users upload a photo of themselves and enter personal information such as their name and contact details.

[0081] Step 3:

[0082] Terminal

[0083] The device temporarily stores the uploaded facial photo.

[0084] Step 4:

[0085] Terminal

[0086] The device uses a facial recognition algorithm to ensure that the submitted photo is valid, analyzing facial feature points and verifying the quality and format.

[0087] Step 5:

[0088] Terminal

[0089] If the facial recognition algorithm determines that the facial photo is valid, the device sends the verification result to the server.

[0090] Step 6:

[0091] server

[0092] The server stores the confirmed facial photograph and the user's personal information in a database.

[0093] Step 7:

[0094] server

[0095] The server notifies the terminal that the registration has been completed and transmits the result to the terminal.

[0096] Step 8:

[0097] Terminal

[0098] The terminal displays a message to the user indicating that registration is complete.

[0099] 2. Generate Request Validation Process

[0100] Step 1:

[0101] User

[0102] A user or a third party sends an image generation request to a terminal.

[0103] Step 2:

[0104] Terminal

[0105] The terminal temporarily stores the facial photo data included in the request.

[0106] Step 3:

[0107] Terminal

[0108] The terminal sends a verification request to the server.

[0109] Step 4:

[0110] server

[0111] The server runs a facial recognition algorithm to match the facial photo data included in the request with a database.

[0112] Step 5:

[0113] server

[0114] The server uses a facial recognition algorithm to compare the facial photo in the request with those in its database to determine if there is a match.

[0115] Step 6:

[0116] server

[0117] If a matching face photo is found, the server sends the result to the device. If not, it sends a "no match" status to the device.

[0118] 3. Image generation permission / denial process

[0119] Step 1:

[0120] Terminal

[0121] The device receives a notification from the server and stops image generation if a matching face photo is included.

[0122] Step 2:

[0123] Terminal

[0124] The terminal notifies the user or a third party of the reason for the cancellation of image generation.

[0125] Step 3:

[0126] Terminal

[0127] If a "no match" notification is received from the server, the terminal begins the image generation process.

[0128] 4. Face recognition process after generation

[0129] Step 1:

[0130] Terminal

[0131] Once the generated image is complete, the device sends it to the server.

[0132] Step 2:

[0133] server

[0134] The server then runs a facial recognition algorithm to match the generated image again against the database.

[0135] Step 3:

[0136] server

[0137] A facial recognition algorithm is used to check whether the face in the generated image matches a face registered in a database.

[0138] Step 4:

[0139] server

[0140] If a matching face is found, the server invalidates the generated image and notifies the device of the result.

[0141] Step 5:

[0142] Terminal

[0143] The terminal notifies the user that the image has been invalidated and deletes the image.

[0144] Step 6:

[0145] server

[0146] On the other hand, if no match is found, the server notifies the terminal that there is no match and returns the generated image to the terminal.

[0147] Step 7:

[0148] Terminal

[0149] The terminal returns the generated image to the user and notifies them that it is available for use.

[0150] Through the above processing steps, it is possible to prevent the user's facial photo from being misused by the generating AI, ensuring privacy and safety.

[0151] Example 1

[0152] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0153] Conventional image generation systems pose a risk that specific individuals may have their facial images misused using generation AI. Furthermore, there is a possibility that the generated images may be tampered with or misused, and there is a lack of means to protect personal privacy and security. This leaves users concerned about the misuse of their facial images. The present invention aims to provide a system that prevents specific individuals from misusing their facial images, allowing them to use image generation services with peace of mind.

[0154] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0155] In this invention, the server includes means for registering facial photos, means for storing the registered facial photos and personal information in a database, means for comparing the photo with the database when an image generation request is made, means for allowing or denying image generation based on the comparison result, means for comparing the generated image with the database again, means for halting image generation if the photo matches the blacklist and notifying the user, and means for comparing the generated image with the database again and returning the generated image if the photo does not match. This makes it possible to prevent the facial photos of specific individuals from being misused by AI generation and to protect privacy safely and effectively.

[0156] "Means for registering face photos" refers to a function that allows users to upload their own face photos to the system through a dedicated application or website.

[0157] "Means for saving to a database" is a function that stores the uploaded facial photo and the user's personal information in a database on the server.

[0158] The "matching means" is a function that, when an image generation request is made, compares the facial photo data included in the request with registered facial photos in the database to confirm whether they match.

[0159] The "means for permitting or denying image generation" is a function that, based on the comparison result, stops image generation if the facial photograph matches the blacklist, and allows image generation to proceed if it does not match.

[0160] The "means for re-matching the generated image" is a function for re-matching the generated image with the database to check whether it matches a facial photograph of a specific person.

[0161] A "blacklist" is a database that manages registered facial photo data and contains a list of facial photos that may be misused in order to prevent fraudulent use.

[0162] The "notification means" is a function that notifies the user or a third party of the reason when a facial photograph matches a blacklist or when a generated image is invalidated.

[0163] The "image generation process" is a series of processes that uses a generative AI model to generate a new image based on input prompt text and image data.

[0164] A "generative AI model" is an artificial intelligence algorithm or framework for generating new images or content based on input data.

[0165] This invention relates to a system that prevents specific individuals from misusing their own facial photos in AI generation. This system reduces the risk of users' facial photo data being used fraudulently and protects their privacy.

[0166] Face photo registration process

[0167] User

[0168] Users access a dedicated application or website and upload a photo of themselves, along with their personal information such as name and contact details, allowing the system to collect all the necessary data in one place.

[0169] Terminal

[0170] The device temporarily stores the face photo uploaded by the user. Then, it uses a facial recognition algorithm (e.g., OpenCV or FaceNet) to verify whether the uploaded face photo is appropriate. Specifically, it analyzes facial feature points (e.g., the positions of the eyes, nose, and mouth) to ensure that the face photo is valid.

[0171] server

[0172] The server saves the verified facial photo and the user's personal information in a database (for example, MySQL (registered trademark) or MongoDB). After saving is complete, the server sends a notification of registration completion to the device, and the device displays a message to the user that registration is complete.

[0173] Generate Request Validation Process

[0174] Terminal

[0175] When a user or a third party sends an image generation request, the device temporarily stores the facial photo data included in the request.The device then sends a matching request to the server to match the facial photo stored in the database with the facial photo included in the request.

[0176] server

[0177] The server receives the matching request and matches the face photo included in the request with the registered face photos in the database. It uses a facial recognition algorithm to compare facial feature points. If the requested face photo matches the blacklist, it notifies the device of the result. If not, it returns a "no match" status to the device.

[0178] Image generation allow / deny process

[0179] Terminal

[0180] The device receives a notification from the server and stops image generation if a matching face is found in the blacklist. It also notifies the user or a third party of the reason for the stop and terminates the generation process. On the other hand, if it receives a notification that there is no match, it continues the image generation process.

[0181] Face recognition process after generation

[0182] Terminal

[0183] After the generated image is completed, the terminal sends the image back to the server for verification.

[0184] server

[0185] The server then compares the generated image against the database to see if it contains the face of a specific person. If a match is found, the server invalidates the generated image and notifies the user of the reason. If a match is not found, the server notifies the user that there is no match, and the device returns the generated image to the user.

[0186] Specific examples

[0187] A specific example of this system is shown below.

[0188] Face photo registration

[0189] User "A" uploads a photo of his face using a dedicated app, enters his name and contact information, and the device validates the photo using a facial recognition algorithm (e.g., OpenCV), and the server stores it in a database.

[0190] Validating the Generated Request

[0191] A third party "B" attempts to generate an image using a generative AI based on a facial photo of user "A." The request is received by the device, and a matching request is sent to the server. The server compares the request with facial photos in its database, and since a match is confirmed, the device rejects the image generation and notifies third party "B."

[0192] Generated face recognition

[0193] A third party "C" generates an image using a generation AI and sends it to the server. The server compares it with the database and returns "no match," and the generated image is returned to third party "C."

[0194] This allows the system to effectively prevent specific individuals from being misused by the generating AI, thereby protecting privacy and preventing fraudulent behavior.

[0195] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0196] Step 1:

[0197] Uploading a user's face photo

[0198] Input: Face photo data, user personal information (name, contact information)

[0199] Output: Temporarily saved face photo data, user personal information

[0200] How it works: The user accesses a dedicated application or website, uploads a photo of their face, and enters their personal information. The device then temporarily stores this data.

[0201] Step 2:

[0202] Verifying the validity of your photo

[0203] Input: Temporarily saved face photo data

[0204] Output: Validation result

[0205] How it works: The device uses a facial recognition algorithm (such as OpenCV or FaceNet) to analyze the features of the uploaded face photo and check whether it is a valid face photo. It detects feature points such as the positions of the eyes, nose, and mouth to confirm validity.

[0206] Step 3:

[0207] Saving to a database

[0208] Input: Verified face photo data, user personal information

[0209] Output: Face photo and personal information stored in the database

[0210] Specific operation: The server saves the verified face photo data and the user's personal information in the database. Once the saving is complete, the server sends a registration completion notification to the device, and the device displays a registration completion message to the user.

[0211] Step 4:

[0212] Receiving and temporarily saving image generation requests

[0213] Input: Image generation request, facial photo data included in the request

[0214] Output: Temporarily saved face photo data

[0215] Specific operation: When an image generation request is sent from a user or a third party, the terminal receives the request and temporarily stores the facial photo data included in the request.

[0216] Step 5:

[0217] Database matching request

[0218] Input: Temporarily saved face photo data

[0219] Output: Matching request

[0220] Specific operation: The terminal sends a matching request to the server to request that the facial photo stored in the database be matched with the facial photo included in the request.

[0221] Step 6:

[0222] Face photo matching process

[0223] Input: Matching request, face photo data stored in the database

[0224] Output: Match result (match or no match)

[0225] Specific operation: The server compares the facial photo data stored in the database with the facial photo data included in the matching request. It uses a facial recognition algorithm to compare facial features. If the requested facial photo matches the blacklist, it notifies the device of the result. If it does not match, it sends a "no match" status.

[0226] Step 7:

[0227] Image Generation Permission or Cancellation Notice

[0228] Input: Matching result

[0229] Output: Image generation approval or rejection notification

[0230] Specific operation: The device receives a notification from the server, and if a face matching the blacklist is found, it stops image generation. It also notifies the user or a third party of the reason and terminates the generation process. If it receives a "no match" notification, it continues the image generation process.

[0231] Step 8:

[0232] Verifying the generated image

[0233] Input: Generated image

[0234] Output: Verification result (valid or invalid), final output image

[0235] Specific operation: Once an image is generated, the device sends it back to the server, which then compares it with the database. It checks to see if it contains the face of a specific person, and if a match is found, it invalidates the generated image and notifies the user of the reason. If there is no match, the server notifies the user that there is no match and returns the generated image to the user.

[0236] (Application example 1)

[0237] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0238] In today's world, many individuals upload their facial photos to social media and other online platforms, increasing the risk that these photos will be misused by generative AI. Such misuse infringes on individuals' privacy and sometimes results in defamation. Furthermore, existing security systems lack automation for the registration and verification process of facial photos, and therefore are unable to provide fast and efficient protection. Therefore, there is a need for the development of a reliable, real-time system to prevent the misuse of individuals' facial photos by generative AI.

[0239] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0240] In this invention, the server includes means for registering a facial photo, means for saving the registered facial photo in a database, means for comparing the facial photo with the database when an image generation request is made, means for permitting or rejecting image generation based on the comparison result, means for comparing the generated image with the database again, means for registering and verifying the facial photo using an application installed on the smartphone, and means for transmitting the facial photo and verification result to the server in real time. This effectively prevents the misuse of individuals' facial photos, making it possible to protect privacy and suppress fraudulent activities.

[0241] A "means for registering a facial photo" is a device or software that allows a user to upload a facial photo to the system and temporarily store the photo within the system.

[0242] The "means for storing in a database" refers to a device or program that has the function of safely and efficiently storing registered facial photographs and related personal information in a database.

[0243] "Means for matching with a database when requesting image generation" refers to a system with a comparison function that checks whether the requested facial photo matches a photo already registered in the database before the generation AI generates the image.

[0244] The "means for permitting or denying image generation based on the matching result" refers to a system that has the function of executing a process that denies image generation if the facial photograph matches as a result of the matching, and permits image generation if the facial photograph does not match.

[0245] "Means for comparing the generated image with the database again" refers to a system that has a process for checking whether the face contained in the generated image matches a facial photograph in the database, and if so, invalidating the generated image.

[0246] "Means for registering and verifying facial photographs using an application installed on a smartphone" refers to a system that allows users to register facial photographs using a dedicated application that runs on a smartphone, and has the function of verifying whether the photograph is valid.

[0247] The "means for transmitting facial photographs and verification results to a server in real time" refers to a system that has a communication protocol for instantly transmitting facial photographs and verification results to a server via a smartphone application and storing them in a database on the server side.

[0248] This invention is a system for preventing specific individuals from misusing their facial photos in AI generation. This system consists of a smartphone application and a server system. This system operates as follows.

[0249] First, a user registers a face photo using a dedicated application installed on their smartphone. The user then takes a photo of their face or selects an existing photo and enters their personal information (name, contact details, etc.). The application then runs a facial recognition algorithm (using the OpenCV library, for example) to verify that the photo is valid. Once verified, the face photo and personal information are sent to a server in real time and stored in a database.

[0250] Next, when a request for image generation is made using the generation AI, the device receives the request and temporarily stores the facial photo included in the request.The device then sends the facial photo data to the server, which then compares it with registered facial photos in its database.If the comparison results in a match, the server sends a notification to the device rejecting image generation, and the device notifies the user or a third party of this.If there is a mismatch, the device allows image generation.

[0251] The generated image also undergoes the same facial recognition process. The device sends the generated image to the server, which then checks it against the database again. If a match is found, the image is invalidated and the user is notified. If a match is not found, the generated image is returned to the user.

[0252] Consider the following scenario: A user completes the facial photo registration process, and then a third party attempts to generate a pornographic image. However, the server-side verification finds that the image matches the user's facial photo, and the attempt is rejected. The generated image is also verified by the server, and if a match is found, the image is invalidated and the user is notified.

[0253] Examples of prompts that demonstrate the operation of this system include the following:

[0254] "Please verify that this photo matches a registered person in our database. If we suspect abuse, we will stop the generation and notify you."

[0255] As a result, the present invention effectively prevents personal facial photos from being misused by AI generation, thereby protecting privacy and preventing fraudulent activities.

[0256] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0257] Step 1:

[0258] A user uses a smartphone application to take or select a face photo and enter personal information (such as name and contact details). The application then temporarily stores the entered face photo and personal information on the device.

[0259] Input: User's photo, name, contact information

[0260] Data processing: taking or selecting a photo of your face, entering personal information

[0261] Output: Temporarily saved face photo and personal information

[0262] Step 2:

[0263] The device uses a facial recognition algorithm (such as the OpenCV library) to verify whether the registered face photo is valid. The facial recognition algorithm analyzes facial feature points to determine whether the photo is valid.

[0264] Input: Temporarily saved face photo

[0265] Data calculation: Apply face recognition algorithm to analyze facial feature points

[0266] Output: Judgment result of whether the face photo is valid or not

[0267] Step 3:

[0268] The device sends the facial photo and personal information that are determined to be valid to the server, which then stores this information in a database.

[0269] Input: Valid face photo and personal information

[0270] Data processing: Send face photo and personal information to the server

[0271] Output: Face photo and personal information stored in the database

[0272] Step 4:

[0273] The device receives an image generation request using the generative AI model, temporarily stores the facial photo data included in the generation request, and then transmits the facial photo data to the server.

[0274] Input: Image generation request, facial photo data included in the generation request

[0275] Data processing: Temporarily save face photo data and send it to the server

[0276] Output: Facial photo data sent to the server

[0277] Step 5:

[0278] The server matches the submitted facial photo data with those stored in its database, using a facial recognition algorithm to compare facial features and determine whether there is a match.

[0279] Input: Requested face photo data, face photos in the database

[0280] Data calculation: Apply face recognition algorithm and compare feature points

[0281] Output: Match result (match or no match)

[0282] Step 6:

[0283] Based on the result of the comparison, the server notifies the terminal whether to allow or deny image generation. If the comparison results are a match, image generation is denied, and if they are not a match, image generation is allowed.

[0284] Input: Matching result

[0285] Data Processing: Generating Accept or Deny Notifications

[0286] Output: Notification to device (allow or deny)

[0287] Step 7:

[0288] The terminal receives the notification from the server and notifies the user or a third party based on the result. If image generation is rejected, the terminal notifies the user of the reason and stops image generation.

[0289] Input: Notification from the server (allow or deny)

[0290] Data processing: generating notifications for users

[0291] Output: Notification to the user or third party

[0292] Step 8:

[0293] Once the generated image is complete, the device sends it back to the server, which checks it against the database and compares facial features again to determine if there is a match.

[0294] Input: Generated image

[0295] Data calculation: Apply face recognition algorithm and compare feature points

[0296] Output: Match result (match or no match)

[0297] Step 9:

[0298] The server validates or invalidates the generated image based on the results of the second check. If the facial photo matches, the image is invalidated and the device is notified of this. If there is a mismatch, the generated image is retained.

[0299] Input: Matching result

[0300] Data processing: Activation or deactivation processing, notification generation

[0301] Output: Notifications to the device (enabled or disabled)

[0302] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0303] This invention combines a system that prevents specific individuals from misusing their facial photos for AI generation with an emotion engine that recognizes the user's emotions. Below, we will explain the specific processing of the system's program and provide details with concrete examples.

[0304] 1. Face photo registration and emotion recognition process

[0305] User

[0306] Users access a dedicated application or website to register their facial photo with the system. They upload their facial photo and enter personal information (such as name and contact details). At the same time, the emotion engine detects and records the user's emotional state.

[0307] Terminal

[0308] The device temporarily stores the uploaded facial photo, and also records the emotion data detected by the emotion engine.

[0309] server

[0310] The server uses a facial recognition algorithm to verify that the uploaded facial photo is valid. After verification, the facial photo and emotional data are stored in a database. After registration is complete, the server notifies the device, and the device displays a message to the user indicating registration is complete.

[0311] 2. Request Generation and Sentiment Matching Process

[0312] Terminal

[0313] A user or a third party sends an image generation request to a device. Not only the facial photo data included in the request, but also the emotion data is temporarily stored for comparison.

[0314] server

[0315] A matching request is sent to the server, and the facial photo data and emotion data are matched against the database. The facial recognition algorithm compares facial feature points, and the emotion engine matches the emotional state.

[0316] server

[0317] If the facial photo and emotion data match the blacklist, the server notifies the device. If they do not match, it returns a "no match" status to the device.

[0318] 3. Image generation permission / denial process

[0319] Terminal

[0320] The device receives a notification from the server and, if a matching facial photo or emotional state is found, stops image generation. It notifies the user or a third party of the reason for the stop. If a "no match" notification is received, the device starts the image generation process.

[0321] 4. Post-generation emotion recognition and face recognition process

[0322] Terminal

[0323] Once the generated image is complete, it is sent to the server and checked again.

[0324] server

[0325] The server runs a facial recognition algorithm and emotion engine to match the submitted generated image against a database to see if the face and emotional state in the generated image match the registered face.

[0326] server

[0327] If a matching face or specific emotional state is detected, the server invalidates the generated image and notifies the device of the result. If no match is found, the server notifies the device of "no match" and returns the generated image.

[0328] Terminal

[0329] The terminal receives the notification from the server and invalidates the generated image if necessary, or returns the generated image to the user if there is no problem with the image and notifies the user that it is available for use.

[0330] Specific examples

[0331] 1. Facial photo registration and emotion recognition

[0332] User "A" uploads a photo of his / her face using a dedicated app and registers emotional data along with necessary personal information. The device verifies the data using a facial recognition algorithm and emotion engine, and the server stores it in a database.

[0333] 2. Request Generation and Sentiment Matching

[0334] A third party "B" sends a request to generate an image using a facial photo. The device temporarily stores the facial photo and emotion information and sends a matching request to the server. The server verifies the photo using a facial recognition algorithm and emotion engine, and if the information matches that of user "A," it notifies the device that generation is rejected.

[0335] 3. Post-generation emotion recognition and face recognition

[0336] After the third party "C" generates an image using the generation AI, it sends it to the server. The server then uses a facial recognition algorithm and emotion engine to check the image again, and if there is no match, it notifies the device. The device then returns the image to the third party "C."

[0337] In this way, the present invention effectively prevents a specific individual's facial photograph and emotional state from being misused by the generation AI, ensuring privacy and safety.

[0338] The processing flow will be explained below.

[0339] 1. Face photo registration and emotion recognition process

[0340] Step 1:

[0341] User

[0342] Users access a dedicated app or website and begin the process of registering their face photo.

[0343] Step 2:

[0344] User

[0345] Users upload a photo of themselves and enter personal information such as their name and contact details.

[0346] Step 3:

[0347] Terminal

[0348] The device will temporarily store the uploaded facial photo.

[0349] Step 4:

[0350] Terminal

[0351] An emotion engine on the device detects the user's emotional state during upload, such as whether the user is feeling a particular emotion, such as relief, surprise, anger, or sadness.

[0352] Step 5:

[0353] Terminal

[0354] Emotion data detected by the emotion engine is also recorded.

[0355] Step 6:

[0356] server

[0357] The server uses a facial recognition algorithm to verify that the uploaded facial photo is valid.

[0358] Step 7:

[0359] server

[0360] After confirmation, the server stores the facial photo, emotional data, and personal information in a database.

[0361] Step 8:

[0362] server

[0363] The server sends a notification of registration completion to the terminal, and the terminal displays a completion message to the user.

[0364] 2. Request Generation and Sentiment Matching Process

[0365] Step 1:

[0366] User

[0367] A user or third party submits an image generation request.

[0368] Step 2:

[0369] Terminal

[0370] The facial photo data and emotion data included in the request are temporarily stored.

[0371] Step 3:

[0372] Terminal

[0373] The device sends a matching request to the server, including a facial photo and emotion data.

[0374] Step 4:

[0375] server

[0376] The server runs a facial recognition algorithm and matches the face photo in the request with those in its database.

[0377] Step 5:

[0378] server

[0379] The emotion engine is used to match the emotion data of the request with the emotion data in the database.

[0380] Step 6:

[0381] server

[0382] It uses facial recognition algorithms and emotion engines to determine whether there is a match with the database.

[0383] Step 7:

[0384] server

[0385] If a matching face photo or emotion data is found, the server notifies the device of the result. If no match is found, a "no match" status is sent to the device.

[0386] 3. Image generation permission / denial process

[0387] Step 1:

[0388] Terminal

[0389] The device receives a notification from the server and stops image generation if a matching facial photo or emotional state is found.

[0390] Step 2:

[0391] Terminal

[0392] The terminal notifies the user or a third party of the reason for the cancellation of image generation.

[0393] Step 3:

[0394] Terminal

[0395] If a "no match" notification is received from the server, the image generation process begins.

[0396] 4. Post-generation emotion recognition and face recognition process

[0397] Step 1:

[0398] Terminal

[0399] Once the generated image is complete, it is sent to the server and verified again.

[0400] Step 2:

[0401] server

[0402] The server then runs a facial recognition algorithm and emotion engine to match the generated image against the database again.

[0403] Step 3:

[0404] server

[0405] A facial recognition algorithm is used to check whether the face in the generated image matches a face registered in a database.

[0406] Step 4:

[0407] server

[0408] An emotion engine is used to check whether the face in the generated image is in a particular emotional state.

[0409] Step 5:

[0410] server

[0411] If a matching face or a specific emotional state is detected, the server invalidates the generated image and notifies the device of the result.

[0412] Step 6:

[0413] Terminal

[0414] The terminal notifies the user that the image has been invalidated and deletes the image.

[0415] Step 7:

[0416] server

[0417] If the verification does not confirm a match, the server notifies the terminal that there is no match and returns the generated image to the terminal.

[0418] Step 8:

[0419] Terminal

[0420] The terminal returns the generated image to the user and notifies them that it is available for use.

[0421] Through the above processing steps, the present invention can prevent both the user's facial photo and emotional state from being misused by the generating AI, thereby ensuring privacy and safety.

[0422] Example 2

[0423] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0424] When preventing a specific individual from misusing their own facial photograph, if the validity of the registered facial photograph and the emotional data are not sufficiently verified, the image may be generated fraudulently. As a result, the risk of violating the user's privacy and emotional state increases. There is a need for technology that solves this problem and prevents the fraudulent use of facial photographs and emotional data.

[0425] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0426] In this invention, the server includes: means for a user to upload their own facial photo and input personal information and emotion data; means for the terminal to temporarily store the uploaded facial photo and detected emotion data; means for the server to verify the validity of the facial photo using a facial recognition algorithm and store the facial photo and emotion data in a database; means for a user or a third party to send an image generation request and temporarily store the facial photo and emotion data included in the request; means for the server to compare the sent facial photo and emotion data with the database and allow or deny image generation based on the comparison result; and means for resending the generated image and for the server to compare it using a facial recognition algorithm and emotion engine. This effectively prevents the fraudulent use of the facial photo and emotion data and ensures the user's privacy and safety.

[0427] "User" refers to an entity that uses the system to register their own facial photograph and make an image generation request.

[0428] A "terminal" is a device that allows a user to access the system and has the function of temporarily storing facial photographs and emotional data.

[0429] "Server" refers to the central computer system that verifies, stores, and matches facial photos and emotional data.

[0430] A "face photo" refers to an image of a specific individual's face, and is used for personal authentication and emotion recognition.

[0431] "Personal Information" refers to personal data such as a user's name, contact details, etc.

[0432] "Emotion data" refers to data that indicates the user's emotional state detected by the emotion engine from a facial photograph.

[0433] A "facial recognition algorithm" refers to a computational method for extracting feature points from a facial photograph and identifying a specific individual.

[0434] An "emotion engine" refers to a system that recognizes a user's emotions from a facial photograph and records that information as data.

[0435] A "database" refers to a system for systematically storing and managing facial photographs, personal information, emotional data, etc.

[0436] "Image generation request" refers to a request sent by a user or third party to the system to generate a new image based on a facial photograph.

[0437] A "blacklist" refers to a restricted list used to prevent unauthorized use of registered facial photos and emotional data.

[0438] The present invention is a system for preventing specific individuals from having their facial photographs misused. This system controls and manages a series of processes, from the stage where a user uploads their facial photograph and registers personal information and emotional data, to the stage where the generated image is reconfirmed to ensure its appropriateness.

[0439] 1. The process for users to register their face photos

[0440] Users access a dedicated application or website and upload a photo of their face. When uploading, they enter personal information (name, contact details, etc.) as well as emotion data that the emotion engine will detect. This information is temporarily stored on the device at the initial stage.

[0441] The emotion engine can use, for example, the Emotion API from Microsoft® Azure® Cognitive Services, which detects emotions from uploaded images and records them as data.

[0442] 2. Storing facial photos and emotion data in a database

[0443] The device temporarily stores the received facial photo and emotion data and sends them to the server.

[0444] The server uses a facial recognition algorithm (e.g., OpenCV library) to verify that the face photo is valid. After verification is complete, the face photo and emotion data are stored in a database. This ensures that the user's face photo and emotion data are managed securely.

[0445] 3. Processing a creation request

[0446] When a user or a third party sends an image generation request to a device, the request includes a facial photo and emotion data, which are temporarily stored on the device and later sent to a server.

[0447] The server compares the received facial photo and emotion data with the data in its database. Using a facial recognition algorithm and emotion engine, the comparison can be made with greater accuracy. If this process results in a match between the facial photo and emotion data and the blacklist, the server immediately sends a generation rejection notification to the device.

[0448] 4. Recheck the generated image

[0449] Once the generated image is complete, the device sends it back to the server and checks it against the database.

[0450] The server activates the facial recognition algorithm and emotion engine to check whether the face and emotional state of the generated image match the registered data. If they match, the generated image is invalidated and notified to the device. If they do not match, the device is notified of "no match" and the image is returned to the user or a third party as valid.

[0451] Specific examples

[0452] 1. Facial photo registration and emotion recognition

[0453] For example, a user can use a dedicated application to upload a photo of their face and register emotional data along with the necessary personal information. The emotion engine detects emotions such as "joy" from the user's photo, and this data is temporarily stored on the device before being sent to the server. At this stage, the facial recognition algorithm works to confirm the validity of the photo.

[0454] 2. Request Generation and Sentiment Matching

[0455] When a third party requests image generation using a face photo registered in the database, the device temporarily stores the face photo and emotion data and sends them to the server. The server then compares this with existing data in the database and sends a notification to the device, such as "This face photo has already been registered."

[0456] 3. Post-generation emotion recognition and face recognition

[0457] After the image is generated using generative AI, it is sent to the server, which checks it against information in its database. If there is no match, the image is returned to the device and the user is notified that it is safe to use.

[0458] Prompt Sentence Examples

[0459] "Please tell me the process for registering a user's face photo and emotion."

[0460] "How do you verify third-party image generation requests?"

[0461] "Please tell us about the process of image authentication and emotion recognition after generation."

[0462] As a result, the present invention can effectively prevent the unauthorized use of facial photographs and emotion data, and ensure the privacy and safety of users.

[0463] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0464] Program processing steps

[0465] Step 1: Enter your photo and personal information

[0466] Users access a dedicated application or website, upload a photo of their face, and enter personal information (such as name and contact details). The emotion engine analyzes the photo and obtains the user's emotional state.

[0467] Input: Face photo, personal information

[0468] Output: Facial photo data, personal information, emotional data

[0469] Specific operation: The user presses the "Upload face photo" button, selects an image file, then enters the necessary information in the "Enter personal information" form and presses the "Submit" button.

[0470] Step 2: Temporarily save data

[0471] The device temporarily stores the facial photo, personal information, and emotional data entered by the user.

[0472] Input: Facial photo data, personal information, emotional data

[0473] Output: Temporarily saved face photo data, personal information, emotional data

[0474] Specific operation: Temporarily saves data in local storage within the application.

[0475] Step 3: Verify the validity of the photo

[0476] The server receives the facial photo sent from the device and verifies its validity using a facial recognition algorithm. If it is valid, the facial photo data, personal information, and emotional data are stored in a database.

[0477] Input: Facial photo data, personal information, emotional data

[0478] Output: Validation result, if valid the data is saved to the database

[0479] Specific operation: The server extracts feature points from the facial photo and analyzes them using a facial recognition algorithm (e.g., OpenCV).

[0480] Step 4: Registration completion notification

[0481] The server notifies the terminal that the validity verification of the face photo has been completed, and the terminal displays a registration completion message to the user.

[0482] Input: Validation result

[0483] Output: Registration completion notification

[0484] Specific operation: The server sends a "Registration complete" notification to the device, and a "Registration complete" pop-up is displayed on the device.

[0485] Step 5: Sending an image generation request

[0486] A user or a third party makes an image generation request, attaches a face photo and emotion data, and sends it to a terminal.

[0487] Input: Image generation request, facial photo data, emotion data

[0488] Output: Face photo data, emotion data, generation request

[0489] Specific operation: Enter the required information in the image generation form and press the "Send generation request" button.

[0490] Step 6: Temporarily save data

[0491] The device temporarily stores the requested facial photo and emotional data.

[0492] Input: face photo data, emotion data, generation request

[0493] Output: Temporarily saved face photo data, emotion data, generation request

[0494] Specific operation: Temporarily save data in the device's memory.

[0495] Step 7: Check against the database

[0496] The server compares the facial photo data and emotion data received from the device with the database, using a facial recognition algorithm and emotion engine to confirm whether the data matches the registered data.

[0497] Input: Facial photo data, emotion data, information in the database

[0498] Output: Matching result (match / mismatch)

[0499] Specific operation: The server analyzes the facial photo and emotional data and compares it with information in a database.

[0500] Step 8: Notification of match results

[0501] The server notifies the terminal of the result of the match. If it matches the registered data, it sends a notification to reject the creation, and if it does not match, it sends a notification to allow the creation.

[0502] Input: Matching result

[0503] Output: Generate approval / rejection notification

[0504] Specific operation: The server communicates the result of "match / no match" to the terminal, and the terminal notifies the user based on the result.

[0505] Step 9: Double-check the generated image

[0506] Once the generated image is complete, the device sends it to the server for another match.

[0507] Input: Generated image

[0508] Output: Verification result (reconfirmation)

[0509] Specific operation: The device sends the generated image to the server and requests reconfirmation.

[0510] Step 10: Notification of the result of matching the generated image

[0511] The server compares the generated image with the information in the database and notifies the terminal of the result of the comparison. If there is a match, the generated image is invalidated; if not, the image is allowed to be used.

[0512] Input: Generated image, information in the database

[0513] Output: Matching result (match / mismatch)

[0514] Specific operation: The server analyzes the generated image, compares it with the database, and notifies the device of the results.

[0515] Step 11: Image Usage Notification

[0516] The device receives notifications from the server and invalidates the generated image if necessary. If there is a notification of no match, the device returns the generated image to the user and notifies them that it is available.

[0517] Input: Verification result (reconfirmation)

[0518] Output: Usage notification / Invalidation notification

[0519] Specific behavior: Display a "Generated images are available" or "Generated images are invalid" message to the user.

[0520] This effectively prevents the unauthorized use of facial photos and emotional data of specific individuals, ensuring the privacy and safety of users.

[0521] (Application example 2)

[0522] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0523] The present invention aims to prevent the misuse of facial photographs of specific individuals in image generation using generative AI, and to further strengthen security by using emotional data. The problem to be solved by this invention is to protect individual privacy and data security, and effectively prevent fraudulent image generation.

[0524] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for registering a facial photograph, means for saving the registered facial photograph in a database, means for comparing the facial photograph with the database when an image generation request is made, means for permitting or rejecting image generation based on the comparison result, means for comparing the generated image with the database again, emotion data recording means for detecting the user's emotional state and recording it together with the facial photograph, means for comparing both the facial photograph and emotion data, and means for notifying the user or a third party based on the comparison result. This reduces the risk of a specific individual's facial photograph being misused, and using emotion data can further strengthen security.

[0525] The "means for registering a face photo" is a function that allows a user to upload a face photo to the system and register it along with personal information.

[0526] The "means for storing registered facial photographs in a database" is a function for safely storing registered facial photographs and related personal information in a database.

[0527] The "means for checking against a database when an image generation request is made" is a function for checking the facial photo data included in the request against registered data in a database when an image generation request is made.

[0528] The "means for permitting or denying image generation based on the result of comparison" is a function for determining whether to permit or deny image generation based on the result of comparison with the database.

[0529] The "means for checking the generated image against the database again" is a function for checking the generated image against the registered data in the database again to confirm safety.

[0530] The "emotion data recording means for detecting the user's emotional state and recording it together with the facial photograph" is a function for detecting the user's emotional state at the same time as registering the facial photograph and recording that data together with the facial photograph.

[0531] "Means for matching both facial photographs and emotional data" is a function that, upon request, matches both facial photographs and emotional data against a database to reduce the risk of fraudulent use.

[0532] The "means for notifying the user or a third party based on the collation result" is a function for notifying the user or a third party of the result when the collation result is a match.

[0533] This invention is a system that aims to prevent specific individuals from misusing their facial photos in AI generation. This system prevents unauthorized use during image generation through a series of processes in which users register their facial photos and record emotional data. The roles of the server, terminal, and user are as follows:

[0534] First, users upload a photo of themselves to a dedicated application or website and provide personal information (such as name and contact details). In addition, an emotion recognition engine detects the user's emotional state, and this data is also recorded. All of this information is temporarily stored on the device and then sent to a server.

[0535] The server uses a facial recognition algorithm and emotional data to verify that the uploaded facial photo is valid and authentic. Once verification is complete, the facial photo and emotional data are stored in a database. Once registration is complete, the server notifies the device, which then displays a message to the user indicating registration is complete.

[0536] Next, when a user or a third party sends an image generation request, the request includes facial photo data and emotion data. The device temporarily saves the request and sends a matching request to the server. The server matches the facial photo and emotion data in the database. If the matching result is a match, it stops image generation and notifies the user or third party of the reason. If there is no match, image generation is allowed.

[0537] After generation, the regenerated image is sent to the server and re-matched using a facial recognition algorithm and emotional data. If the matching facial photo and emotional state match the registered data, the generated image is invalidated and the result is notified to the device. If there is no match, the generated image is returned to the user and their use is permitted.

[0538] For example, when a user registers a face photo, name, and contact information, the system uses a face recognition algorithm to verify whether the face photo is valid. The system also uses an emotion engine to detect the user's emotional state and transmits it to the server. Examples of prompts include:

[0539] Example: "Register a photo of User A to prevent unauthorized use."

[0540] Upload User A.jpg and enter your name, contact information, and emotional state (e.g., happy).

[0541] As described above, the present invention provides a function to prevent unauthorized image generation by using a specific individual's facial photograph and emotional state data, thereby improving the security of personal information and creating an environment where image generation services can be used with peace of mind.

[0542] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0543] Step 1:

[0544] Users access a dedicated application or website and upload a photo of their face. At this time, they also enter personal information (such as name and contact details), and an emotion recognition engine detects the user's emotional state. The entered facial photo data, personal information, and emotional data are temporarily stored on the device.

[0545] Input: Facial photo data, personal information, emotional data

[0546] Output: Facial photo data, personal information, and emotional data temporarily stored on the device

[0547] Step 2:

[0548] The device then sends the stored data to a server, which uses a facial recognition algorithm to verify that the uploaded photo is valid, along with any emotional data.

[0549] Input: Temporarily saved face photo data, personal information, emotional data

[0550] Output: Facial photo data, personal information, and emotional data sent to the server

[0551] Step 3:

[0552] The server stores the received facial photograph and emotion data in a database. After verifying that the facial photograph data and emotion data are valid, the server stores these data in the database.

[0553] Input: Transmitted face photo data, personal information, emotional data

[0554] Output: Facial photo data, personal information, and emotional data stored in a database

[0555] Step 4:

[0556] Once registration is complete, the server notifies the terminal, and the terminal displays a message to the user indicating that registration is complete.

[0557] Input: Facial photo data, personal information, and emotional data stored in the database

[0558] Output: Notification of successful registration, displayed message

[0559] Step 5:

[0560] The user or a third party sends an image generation request, which includes facial photo data and emotion data. The device temporarily stores this data and then sends a matching request to the server.

[0561] Input: Image generation request, facial photo data, emotion data

[0562] Output: Request data temporarily saved on the device, matching request sent

[0563] Step 6:

[0564] The server compares the facial photos in the database with the emotion data and notifies the device of the results. If the comparison results match, the server stops image generation and notifies the device of the reason. If the comparison results do not match, the server allows image generation.

[0565] Input: Matching request sent, face photo data in the database, emotion data

[0566] Output: Matching result, notification of abort if matched, permission to generate if not matched

[0567] Step 7:

[0568] After generation, the regenerated image is sent to the server and re-matched using a facial recognition algorithm and emotional data.

[0569] Input: Generated image data

[0570] Output: Generated image data sent

[0571] Step 8:

[0572] The server compares the generated image data with the database, and if the matching facial photo or emotional state matches the registered data, the generated image is invalidated. The result is notified to the terminal. If there is no match, the generated image is returned to the user.

[0573] Input: Generated image data, face photo data in the database, emotion data

[0574] Output: Matching result, invalidation notification if matched, image returned if not matched

[0575] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0576] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0577] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0578] [Second embodiment]

[0579] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0580] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0581] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0582] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0583] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0584] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0585] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0586] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0587] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0588] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0589] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0590] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0591] This invention relates to a system that prevents specific individuals from misusing their facial photos in AI-generated images. Below, we will explain the specific processing of the system's program and provide details with concrete examples.

[0592] 1. Face photo registration process

[0593] User

[0594] To register their own face photo in the system, users access a dedicated application or website, upload their own face photo, and enter their personal information (name, contact details, etc.).

[0595] Terminal

[0596] The device will temporarily store the uploaded facial photo and use a facial recognition algorithm to verify whether the photo is appropriate. The facial recognition algorithm will analyze the facial features of the uploaded photo to ensure it is a valid facial photo.

[0597] server

[0598] The server stores the user's confirmed facial photo and personal information in a database. After registration is complete, the server notifies the device, and the device displays a message to the user indicating that registration is complete.

[0599] 2. Generate Request Validation Process

[0600] Terminal

[0601] The device receives an image generation request from a user or a third party, temporarily stores the facial photo data included in the request, and then sends a matching request to the server.

[0602] server

[0603] The server receives the matching request and matches the face photo in the request with the face photos stored in its database, using a facial recognition algorithm to compare facial feature points and see if there is a match.

[0604] If the server finds a face photo that matches the blacklist, it notifies the device of the result. If there is no match, it returns a "no match" status to the device.

[0605] 3. Image generation permission / denial process

[0606] Terminal

[0607] The device receives a notification from the server and stops image generation if a face matching the blacklist is found, notifies the user or a third party of the reason for the stop, and terminates the generation process.

[0608] If a "no match" notification is received from the server, the device proceeds with the image generation process.

[0609] 4. Face recognition process after generation

[0610] Terminal

[0611] Once the generated image is complete, the terminal sends it back to the server for verification.

[0612] server

[0613] The server checks the generated image against a database to see if it contains a specific person's face, and if a match is found, invalidates the image and notifies the user of the reason.

[0614] If no match is found, the server notifies the terminal of "no match" and the terminal returns the generated image to the user.

[0615] Specific examples

[0616] 1. Register your face photo

[0617] User "X" uploads a photo of their face via a dedicated app and enters the necessary personal information. The device uses a facial recognition algorithm to verify that the photo is valid, and the server stores it in a database.

[0618] 2. Validating the Generated Request

[0619] A third party "Y" attempts to generate an obscene image based on a specific facial photograph. The device that receives the request sends a matching request to the server, which checks the database and confirms that the facial photograph matches that of user "X." The device rejects the image generation and notifies third party "Y."

[0620] 3. Face recognition after generation

[0621] A third party, "Z," uses a generation AI to generate an image and sends it to the server. The server checks it against a database, finds no match, sends a "no match" notification to the device, and returns the image to the third party, "Z."

[0622] In this way, the present invention effectively prevents specific individuals from being misused by the generation AI, thereby protecting privacy and preventing fraudulent activities.

[0623] The processing flow will be explained below.

[0624] 1. Face photo registration process

[0625] Step 1:

[0626] User

[0627] Users access a dedicated app or website and begin the process of registering their face photo.

[0628] Step 2:

[0629] User

[0630] Users upload a photo of themselves and enter personal information such as their name and contact details.

[0631] Step 3:

[0632] Terminal

[0633] The device temporarily stores the uploaded facial photo.

[0634] Step 4:

[0635] Terminal

[0636] The device uses a facial recognition algorithm to ensure that the submitted photo is valid, analyzing facial feature points and verifying the quality and format.

[0637] Step 5:

[0638] Terminal

[0639] If the facial recognition algorithm determines that the facial photo is valid, the device sends the verification result to the server.

[0640] Step 6:

[0641] server

[0642] The server stores the confirmed facial photograph and the user's personal information in a database.

[0643] Step 7:

[0644] server

[0645] The server notifies the terminal that the registration has been completed and transmits the result to the terminal.

[0646] Step 8:

[0647] Terminal

[0648] The terminal displays a message to the user indicating that registration is complete.

[0649] 2. Generate Request Validation Process

[0650] Step 1:

[0651] User

[0652] A user or a third party sends an image generation request to a terminal.

[0653] Step 2:

[0654] Terminal

[0655] The terminal temporarily stores the facial photo data included in the request.

[0656] Step 3:

[0657] Terminal

[0658] The terminal sends a verification request to the server.

[0659] Step 4:

[0660] server

[0661] The server runs a facial recognition algorithm to match the facial photo data included in the request with a database.

[0662] Step 5:

[0663] server

[0664] The server uses a facial recognition algorithm to compare the facial photo in the request with those in its database to determine if there is a match.

[0665] Step 6:

[0666] server

[0667] If a matching face photo is found, the server sends the result to the device. If not, it sends a "no match" status to the device.

[0668] 3. Image generation permission / denial process

[0669] Step 1:

[0670] Terminal

[0671] The device receives a notification from the server and stops image generation if a matching face photo is included.

[0672] Step 2:

[0673] Terminal

[0674] The terminal notifies the user or a third party of the reason for the cancellation of image generation.

[0675] Step 3:

[0676] Terminal

[0677] If a "no match" notification is received from the server, the terminal begins the image generation process.

[0678] 4. Face recognition process after generation

[0679] Step 1:

[0680] Terminal

[0681] Once the generated image is complete, the device sends it to the server.

[0682] Step 2:

[0683] server

[0684] The server then runs a facial recognition algorithm to match the generated image again against the database.

[0685] Step 3:

[0686] server

[0687] A facial recognition algorithm is used to check whether the face in the generated image matches a face registered in a database.

[0688] Step 4:

[0689] server

[0690] If a matching face is found, the server invalidates the generated image and notifies the device of the result.

[0691] Step 5:

[0692] Terminal

[0693] The terminal notifies the user that the image has been invalidated and deletes the image.

[0694] Step 6:

[0695] server

[0696] On the other hand, if no match is found, the server notifies the terminal that there is no match and returns the generated image to the terminal.

[0697] Step 7:

[0698] Terminal

[0699] The terminal returns the generated image to the user and notifies them that it is available for use.

[0700] Through the above processing steps, it is possible to prevent the user's facial photo from being misused by the generating AI, ensuring privacy and safety.

[0701] Example 1

[0702] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0703] Conventional image generation systems pose a risk that specific individuals may have their facial images misused using generation AI. Furthermore, there is a possibility that the generated images may be tampered with or misused, and there is a lack of means to protect personal privacy and security. This leaves users concerned about the misuse of their facial images. The present invention aims to provide a system that prevents specific individuals from misusing their facial images, allowing them to use image generation services with peace of mind.

[0704] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0705] In this invention, the server includes means for registering facial photos, means for storing the registered facial photos and personal information in a database, means for comparing the photo with the database when an image generation request is made, means for allowing or denying image generation based on the comparison result, means for comparing the generated image with the database again, means for halting image generation if the photo matches the blacklist and notifying the user, and means for comparing the generated image with the database again and returning the generated image if the photo does not match. This makes it possible to prevent the facial photos of specific individuals from being misused by AI generation and to protect privacy safely and effectively.

[0706] "Means for registering face photos" refers to a function that allows users to upload their own face photos to the system through a dedicated application or website.

[0707] "Means for saving to a database" is a function that stores the uploaded facial photo and the user's personal information in a database on the server.

[0708] The "matching means" is a function that, when an image generation request is made, compares the facial photo data included in the request with registered facial photos in the database to confirm whether they match.

[0709] The "means for permitting or denying image generation" is a function that, based on the comparison result, stops image generation if the facial photograph matches the blacklist, and allows image generation to proceed if it does not match.

[0710] The "means for re-matching the generated image" is a function for re-matching the generated image with the database to check whether it matches a facial photograph of a specific person.

[0711] A "blacklist" is a database that manages registered facial photo data and contains a list of facial photos that may be misused in order to prevent fraudulent use.

[0712] The "notification means" is a function that notifies the user or a third party of the reason when a facial photograph matches a blacklist or when a generated image is invalidated.

[0713] The "image generation process" is a series of processes that uses a generative AI model to generate a new image based on input prompt text and image data.

[0714] A "generative AI model" is an artificial intelligence algorithm or framework for generating new images or content based on input data.

[0715] This invention relates to a system that prevents specific individuals from misusing their own facial photos in AI generation. This system reduces the risk of users' facial photo data being used fraudulently and protects their privacy.

[0716] Face photo registration process

[0717] User

[0718] Users access a dedicated application or website and upload a photo of themselves, along with their personal information such as name and contact details, allowing the system to collect all the necessary data in one place.

[0719] Terminal

[0720] The device temporarily stores the face photo uploaded by the user. Then, it uses a facial recognition algorithm (e.g., OpenCV or FaceNet) to verify whether the uploaded face photo is appropriate. Specifically, it analyzes facial feature points (e.g., the positions of the eyes, nose, and mouth) to ensure that the face photo is valid.

[0721] server

[0722] The server saves the verified facial photo and the user's personal information in a database (e.g., MySQL or MongoDB). After saving is complete, the server sends a notification of registration completion to the device, and the device displays a message to the user that registration is complete.

[0723] Generate Request Validation Process

[0724] Terminal

[0725] When a user or a third party sends an image generation request, the device temporarily stores the facial photo data included in the request.The device then sends a matching request to the server to match the facial photo stored in the database with the facial photo included in the request.

[0726] server

[0727] The server receives the matching request and matches the face photo included in the request with the registered face photos in the database. It uses a facial recognition algorithm to compare facial feature points. If the requested face photo matches the blacklist, it notifies the device of the result. If not, it returns a "no match" status to the device.

[0728] Image generation allow / deny process

[0729] Terminal

[0730] The device receives a notification from the server and stops image generation if a matching face is found in the blacklist. It also notifies the user or a third party of the reason for the stop and terminates the generation process. On the other hand, if it receives a notification that there is no match, it continues the image generation process.

[0731] Face recognition process after generation

[0732] Terminal

[0733] After the generated image is completed, the terminal sends the image back to the server for verification.

[0734] server

[0735] The server then compares the generated image against the database to see if it contains the face of a specific person. If a match is found, the server invalidates the generated image and notifies the user of the reason. If a match is not found, the server notifies the user that there is no match, and the device returns the generated image to the user.

[0736] Specific examples

[0737] A specific example of this system is shown below.

[0738] Face photo registration

[0739] User "A" uploads a photo of his face using a dedicated app, enters his name and contact information, and the device validates the photo using a facial recognition algorithm (e.g., OpenCV), and the server stores it in a database.

[0740] Validating the Generated Request

[0741] A third party "B" attempts to generate an image using a generative AI based on a facial photo of user "A." The request is received by the device, and a matching request is sent to the server. The server compares the request with facial photos in its database, and since a match is confirmed, the device rejects the image generation and notifies third party "B."

[0742] Generated face recognition

[0743] A third party "C" generates an image using a generation AI and sends it to the server. The server compares it with the database and returns "no match," and the generated image is returned to third party "C."

[0744] This allows the system to effectively prevent specific individuals from being misused by the generating AI, thereby protecting privacy and preventing fraudulent behavior.

[0745] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0746] Step 1:

[0747] Uploading a user's face photo

[0748] Input: Face photo data, user personal information (name, contact information)

[0749] Output: Temporarily saved face photo data, user personal information

[0750] How it works: The user accesses a dedicated application or website, uploads a photo of their face, and enters their personal information. The device then temporarily stores this data.

[0751] Step 2:

[0752] Verifying the validity of your photo

[0753] Input: Temporarily saved face photo data

[0754] Output: Validation result

[0755] How it works: The device uses a facial recognition algorithm (such as OpenCV or FaceNet) to analyze the features of the uploaded face photo and check whether it is a valid face photo. It detects feature points such as the positions of the eyes, nose, and mouth to confirm validity.

[0756] Step 3:

[0757] Saving to a database

[0758] Input: Verified face photo data, user personal information

[0759] Output: Face photo and personal information stored in the database

[0760] Specific operation: The server saves the verified face photo data and the user's personal information in the database. Once the saving is complete, the server sends a registration completion notification to the device, and the device displays a registration completion message to the user.

[0761] Step 4:

[0762] Receiving and temporarily saving image generation requests

[0763] Input: Image generation request, facial photo data included in the request

[0764] Output: Temporarily saved face photo data

[0765] Specific operation: When an image generation request is sent from a user or a third party, the terminal receives the request and temporarily stores the facial photo data included in the request.

[0766] Step 5:

[0767] Database matching request

[0768] Input: Temporarily saved face photo data

[0769] Output: Matching request

[0770] Specific operation: The terminal sends a matching request to the server to request that the facial photo stored in the database be matched with the facial photo included in the request.

[0771] Step 6:

[0772] Face photo matching process

[0773] Input: Matching request, face photo data stored in the database

[0774] Output: Match result (match or no match)

[0775] Specific operation: The server compares the facial photo data stored in the database with the facial photo data included in the matching request. It uses a facial recognition algorithm to compare facial features. If the requested facial photo matches the blacklist, it notifies the device of the result. If it does not match, it sends a "no match" status.

[0776] Step 7:

[0777] Image Generation Permission or Cancellation Notice

[0778] Input: Matching result

[0779] Output: Image generation approval or rejection notification

[0780] Specific operation: The device receives a notification from the server, and if a face matching the blacklist is found, it stops image generation. It also notifies the user or a third party of the reason and terminates the generation process. If it receives a "no match" notification, it continues the image generation process.

[0781] Step 8:

[0782] Verifying the generated image

[0783] Input: Generated image

[0784] Output: Verification result (valid or invalid), final output image

[0785] Specific operation: Once an image is generated, the device sends it back to the server, which then compares it with the database. It checks to see if it contains the face of a specific person, and if a match is found, it invalidates the generated image and notifies the user of the reason. If there is no match, the server notifies the user that there is no match and returns the generated image to the user.

[0786] (Application example 1)

[0787] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0788] In today's world, many individuals upload their facial photos to social media and other online platforms, increasing the risk that these photos will be misused by generative AI. Such misuse infringes on individuals' privacy and sometimes results in defamation. Furthermore, existing security systems lack automation for the registration and verification process of facial photos, and therefore are unable to provide fast and efficient protection. Therefore, there is a need for the development of a reliable, real-time system to prevent the misuse of individuals' facial photos by generative AI.

[0789] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0790] In this invention, the server includes means for registering a facial photo, means for saving the registered facial photo in a database, means for comparing the facial photo with the database when an image generation request is made, means for permitting or rejecting image generation based on the comparison result, means for comparing the generated image with the database again, means for registering and verifying the facial photo using an application installed on the smartphone, and means for transmitting the facial photo and verification result to the server in real time. This effectively prevents the misuse of individuals' facial photos, making it possible to protect privacy and suppress fraudulent activities.

[0791] A "means for registering a facial photo" is a device or software that allows a user to upload a facial photo to the system and temporarily store the photo within the system.

[0792] The "means for storing in a database" refers to a device or program that has the function of safely and efficiently storing registered facial photographs and related personal information in a database.

[0793] "Means for matching with a database when requesting image generation" refers to a system with a comparison function that checks whether the requested facial photo matches a photo already registered in the database before the generation AI generates the image.

[0794] The "means for permitting or denying image generation based on the matching result" refers to a system that has the function of executing a process that denies image generation if the facial photograph matches as a result of the matching, and permits image generation if the facial photograph does not match.

[0795] "Means for comparing the generated image with the database again" refers to a system that has a process for checking whether the face contained in the generated image matches a facial photograph in the database, and if so, invalidating the generated image.

[0796] "Means for registering and verifying facial photographs using an application installed on a smartphone" refers to a system that allows users to register facial photographs using a dedicated application that runs on a smartphone, and has the function of verifying whether the photograph is valid.

[0797] The "means for transmitting facial photographs and verification results to a server in real time" refers to a system that has a communication protocol for instantly transmitting facial photographs and verification results to a server via a smartphone application and storing them in a database on the server side.

[0798] This invention is a system for preventing specific individuals from misusing their facial photos in AI generation. This system consists of a smartphone application and a server system. This system operates as follows.

[0799] First, a user registers a face photo using a dedicated application installed on their smartphone. The user then takes a photo of their face or selects an existing photo and enters their personal information (name, contact details, etc.). The application then runs a facial recognition algorithm (using the OpenCV library, for example) to verify that the photo is valid. Once verified, the face photo and personal information are sent to a server in real time and stored in a database.

[0800] Next, when a request for image generation is made using the generation AI, the device receives the request and temporarily stores the facial photo included in the request.The device then sends the facial photo data to the server, which then compares it with registered facial photos in its database.If the comparison results in a match, the server sends a notification to the device rejecting image generation, and the device notifies the user or a third party of this.If there is a mismatch, the device allows image generation.

[0801] The generated image also undergoes the same facial recognition process. The device sends the generated image to the server, which then checks it against the database again. If a match is found, the image is invalidated and the user is notified. If a match is not found, the generated image is returned to the user.

[0802] Consider the following scenario: A user completes the facial photo registration process, and then a third party attempts to generate a pornographic image. However, the server-side verification finds that the image matches the user's facial photo, and the attempt is rejected. The generated image is also verified by the server, and if a match is found, the image is invalidated and the user is notified.

[0803] Examples of prompts that demonstrate the operation of this system include the following:

[0804] "Please verify that this photo matches a registered person in our database. If we suspect abuse, we will stop the generation and notify you."

[0805] As a result, the present invention effectively prevents personal facial photos from being misused by AI generation, thereby protecting privacy and preventing fraudulent activities.

[0806] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0807] Step 1:

[0808] A user uses a smartphone application to take or select a face photo and enter personal information (such as name and contact details). The application then temporarily stores the entered face photo and personal information on the device.

[0809] Input: User's photo, name, contact information

[0810] Data processing: taking or selecting a photo of your face, entering personal information

[0811] Output: Temporarily saved face photo and personal information

[0812] Step 2:

[0813] The device uses a facial recognition algorithm (such as the OpenCV library) to verify whether the registered face photo is valid. The facial recognition algorithm analyzes facial feature points to determine whether the photo is valid.

[0814] Input: Temporarily saved face photo

[0815] Data calculation: Apply face recognition algorithm to analyze facial feature points

[0816] Output: Judgment result of whether the face photo is valid or not

[0817] Step 3:

[0818] The device sends the facial photo and personal information that are determined to be valid to the server, which then stores this information in a database.

[0819] Input: Valid face photo and personal information

[0820] Data processing: Send face photo and personal information to the server

[0821] Output: Face photo and personal information stored in the database

[0822] Step 4:

[0823] The device receives an image generation request using the generative AI model, temporarily stores the facial photo data included in the generation request, and then transmits the facial photo data to the server.

[0824] Input: Image generation request, facial photo data included in the generation request

[0825] Data processing: Temporarily save face photo data and send it to the server

[0826] Output: Facial photo data sent to the server

[0827] Step 5:

[0828] The server matches the submitted facial photo data with those stored in its database, using a facial recognition algorithm to compare facial features and determine whether there is a match.

[0829] Input: Requested face photo data, face photos in the database

[0830] Data calculation: Apply face recognition algorithm and compare feature points

[0831] Output: Match result (match or no match)

[0832] Step 6:

[0833] Based on the result of the comparison, the server notifies the terminal whether to allow or deny image generation. If the comparison results are a match, image generation is denied, and if they are not a match, image generation is allowed.

[0834] Input: Matching result

[0835] Data Processing: Generating Accept or Deny Notifications

[0836] Output: Notification to device (allow or deny)

[0837] Step 7:

[0838] The terminal receives the notification from the server and notifies the user or a third party based on the result. If image generation is rejected, the terminal notifies the user of the reason and stops image generation.

[0839] Input: Notification from the server (allow or deny)

[0840] Data processing: generating notifications for users

[0841] Output: Notification to the user or third party

[0842] Step 8:

[0843] Once the generated image is complete, the device sends it back to the server, which checks it against the database and compares facial features again to determine if there is a match.

[0844] Input: Generated image

[0845] Data calculation: Apply face recognition algorithm and compare feature points

[0846] Output: Match result (match or no match)

[0847] Step 9:

[0848] The server validates or invalidates the generated image based on the results of the second check. If the facial photo matches, the image is invalidated and the device is notified of this. If there is a mismatch, the generated image is retained.

[0849] Input: Matching result

[0850] Data processing: Activation or deactivation processing, notification generation

[0851] Output: Notifications to the device (enabled or disabled)

[0852] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0853] This invention combines a system that prevents specific individuals from misusing their facial photos for AI generation with an emotion engine that recognizes the user's emotions. Below, we will explain the specific processing of the system's program and provide details with concrete examples.

[0854] 1. Face photo registration and emotion recognition process

[0855] User

[0856] Users access a dedicated application or website to register their facial photo with the system. They upload their facial photo and enter personal information (such as name and contact details). At the same time, the emotion engine detects and records the user's emotional state.

[0857] Terminal

[0858] The device temporarily stores the uploaded facial photo, and also records the emotion data detected by the emotion engine.

[0859] server

[0860] The server uses a facial recognition algorithm to verify that the uploaded facial photo is valid. After verification, the facial photo and emotional data are stored in a database. After registration is complete, the server notifies the device, and the device displays a message to the user indicating registration is complete.

[0861] 2. Request Generation and Sentiment Matching Process

[0862] Terminal

[0863] A user or a third party sends an image generation request to a device. Not only the facial photo data included in the request, but also the emotion data is temporarily stored for comparison.

[0864] server

[0865] A matching request is sent to the server, and the facial photo data and emotion data are matched against the database. The facial recognition algorithm compares facial feature points, and the emotion engine matches the emotional state.

[0866] server

[0867] If the facial photo and emotion data match the blacklist, the server notifies the device. If they do not match, it returns a "no match" status to the device.

[0868] 3. Image generation permission / denial process

[0869] Terminal

[0870] The device receives a notification from the server and, if a matching facial photo or emotional state is found, stops image generation. It notifies the user or a third party of the reason for the stop. If a "no match" notification is received, the device starts the image generation process.

[0871] 4. Post-generation emotion recognition and face recognition process

[0872] Terminal

[0873] Once the generated image is complete, it is sent to the server and checked again.

[0874] server

[0875] The server runs a facial recognition algorithm and emotion engine to match the submitted generated image against a database to see if the face and emotional state in the generated image match the registered face.

[0876] server

[0877] If a matching face or specific emotional state is detected, the server invalidates the generated image and notifies the device of the result. If no match is found, the server notifies the device of "no match" and returns the generated image.

[0878] Terminal

[0879] The terminal receives the notification from the server and invalidates the generated image if necessary, or returns the generated image to the user if there is no problem with the image and notifies the user that it is available for use.

[0880] Specific examples

[0881] 1. Facial photo registration and emotion recognition

[0882] User "A" uploads a photo of his / her face using a dedicated app and registers emotional data along with necessary personal information. The device verifies the data using a facial recognition algorithm and emotion engine, and the server stores it in a database.

[0883] 2. Request Generation and Sentiment Matching

[0884] A third party "B" sends a request to generate an image using a facial photo. The device temporarily stores the facial photo and emotion information and sends a matching request to the server. The server verifies the photo using a facial recognition algorithm and emotion engine, and if the information matches that of user "A," it notifies the device that generation is rejected.

[0885] 3. Post-generation emotion recognition and face recognition

[0886] After the third party "C" generates an image using the generation AI, it sends it to the server. The server then uses a facial recognition algorithm and emotion engine to check the image again, and if there is no match, it notifies the device. The device then returns the image to the third party "C."

[0887] In this way, the present invention effectively prevents a specific individual's facial photograph and emotional state from being misused by the generation AI, ensuring privacy and safety.

[0888] The processing flow will be explained below.

[0889] 1. Face photo registration and emotion recognition process

[0890] Step 1:

[0891] User

[0892] Users access a dedicated app or website and begin the process of registering their face photo.

[0893] Step 2:

[0894] User

[0895] Users upload a photo of themselves and enter personal information such as their name and contact details.

[0896] Step 3:

[0897] Terminal

[0898] The device will temporarily store the uploaded facial photo.

[0899] Step 4:

[0900] Terminal

[0901] An emotion engine on the device detects the user's emotional state during upload, such as whether the user is feeling a particular emotion, such as relief, surprise, anger, or sadness.

[0902] Step 5:

[0903] Terminal

[0904] Emotion data detected by the emotion engine is also recorded.

[0905] Step 6:

[0906] server

[0907] The server uses a facial recognition algorithm to verify that the uploaded facial photo is valid.

[0908] Step 7:

[0909] server

[0910] After confirmation, the server stores the facial photo, emotional data, and personal information in a database.

[0911] Step 8:

[0912] server

[0913] The server sends a notification of registration completion to the terminal, and the terminal displays a completion message to the user.

[0914] 2. Request Generation and Sentiment Matching Process

[0915] Step 1:

[0916] User

[0917] A user or third party submits an image generation request.

[0918] Step 2:

[0919] Terminal

[0920] The facial photo data and emotion data included in the request are temporarily stored.

[0921] Step 3:

[0922] Terminal

[0923] The device sends a matching request to the server, including a facial photo and emotion data.

[0924] Step 4:

[0925] server

[0926] The server runs a facial recognition algorithm and matches the face photo in the request with those in its database.

[0927] Step 5:

[0928] server

[0929] The emotion engine is used to match the emotion data of the request with the emotion data in the database.

[0930] Step 6:

[0931] server

[0932] It uses facial recognition algorithms and emotion engines to determine whether there is a match with the database.

[0933] Step 7:

[0934] server

[0935] If a matching face photo or emotion data is found, the server notifies the device of the result. If no match is found, a "no match" status is sent to the device.

[0936] 3. Image generation permission / denial process

[0937] Step 1:

[0938] Terminal

[0939] The device receives a notification from the server and stops image generation if a matching facial photo or emotional state is found.

[0940] Step 2:

[0941] Terminal

[0942] The terminal notifies the user or a third party of the reason for the cancellation of image generation.

[0943] Step 3:

[0944] Terminal

[0945] If a "no match" notification is received from the server, the image generation process begins.

[0946] 4. Post-generation emotion recognition and face recognition process

[0947] Step 1:

[0948] Terminal

[0949] Once the generated image is complete, it is sent to the server and verified again.

[0950] Step 2:

[0951] server

[0952] The server then runs a facial recognition algorithm and emotion engine to match the generated image against the database again.

[0953] Step 3:

[0954] server

[0955] A facial recognition algorithm is used to check whether the face in the generated image matches a face registered in a database.

[0956] Step 4:

[0957] server

[0958] An emotion engine is used to check whether the face in the generated image is in a particular emotional state.

[0959] Step 5:

[0960] server

[0961] If a matching face or a specific emotional state is detected, the server invalidates the generated image and notifies the device of the result.

[0962] Step 6:

[0963] Terminal

[0964] The terminal notifies the user that the image has been invalidated and deletes the image.

[0965] Step 7:

[0966] server

[0967] If the verification does not confirm a match, the server notifies the terminal that there is no match and returns the generated image to the terminal.

[0968] Step 8:

[0969] Terminal

[0970] The terminal returns the generated image to the user and notifies them that it is available for use.

[0971] Through the above processing steps, the present invention can prevent both the user's facial photo and emotional state from being misused by the generating AI, thereby ensuring privacy and safety.

[0972] Example 2

[0973] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0974] When preventing a specific individual from misusing their own facial photograph, if the validity of the registered facial photograph and the emotional data are not sufficiently verified, the image may be generated fraudulently. As a result, the risk of violating the user's privacy and emotional state increases. There is a need for technology that solves this problem and prevents the fraudulent use of facial photographs and emotional data.

[0975] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0976] In this invention, the server includes: means for a user to upload their own facial photo and input personal information and emotion data; means for the terminal to temporarily store the uploaded facial photo and detected emotion data; means for the server to verify the validity of the facial photo using a facial recognition algorithm and store the facial photo and emotion data in a database; means for a user or a third party to send an image generation request and temporarily store the facial photo and emotion data included in the request; means for the server to compare the sent facial photo and emotion data with the database and allow or deny image generation based on the comparison result; and means for resending the generated image and for the server to compare it using a facial recognition algorithm and emotion engine. This effectively prevents the fraudulent use of the facial photo and emotion data and ensures the user's privacy and safety.

[0977] "User" refers to an entity that uses the system to register their own facial photograph and make an image generation request.

[0978] A "terminal" is a device that allows a user to access the system and has the function of temporarily storing facial photographs and emotional data.

[0979] "Server" refers to the central computer system that verifies, stores, and matches facial photos and emotional data.

[0980] A "face photo" refers to an image of a specific individual's face, and is used for personal authentication and emotion recognition.

[0981] "Personal Information" refers to personal data such as a user's name, contact details, etc.

[0982] "Emotion data" refers to data that indicates the user's emotional state detected by the emotion engine from a facial photograph.

[0983] A "facial recognition algorithm" refers to a computational method for extracting feature points from a facial photograph and identifying a specific individual.

[0984] An "emotion engine" refers to a system that recognizes a user's emotions from a facial photograph and records that information as data.

[0985] A "database" refers to a system for systematically storing and managing facial photographs, personal information, emotional data, etc.

[0986] "Image generation request" refers to a request sent by a user or third party to the system to generate a new image based on a facial photograph.

[0987] A "blacklist" refers to a restricted list used to prevent unauthorized use of registered facial photos and emotional data.

[0988] The present invention is a system for preventing specific individuals from having their facial photographs misused. This system controls and manages a series of processes, from the stage where a user uploads their facial photograph and registers personal information and emotional data, to the stage where the generated image is reconfirmed to ensure its appropriateness.

[0989] 1. The process for users to register their face photos

[0990] Users access a dedicated application or website and upload a photo of their face. When uploading, they enter personal information (name, contact details, etc.) as well as emotion data that the emotion engine will detect. This information is temporarily stored on the device at the initial stage.

[0991] The emotion engine can be, for example, the Emotion API from Microsoft Azure Cognitive Services, which detects emotions from uploaded images and records them as data.

[0992] 2. Storing facial photos and emotion data in a database

[0993] The device temporarily stores the received facial photo and emotion data and sends them to the server.

[0994] The server uses a facial recognition algorithm (e.g., OpenCV library) to verify that the face photo is valid. After verification is complete, the face photo and emotion data are stored in a database. This ensures that the user's face photo and emotion data are managed securely.

[0995] 3. Processing a creation request

[0996] When a user or a third party sends an image generation request to a device, the request includes a facial photo and emotion data, which are temporarily stored on the device and later sent to a server.

[0997] The server compares the received facial photo and emotion data with the data in its database. Using a facial recognition algorithm and emotion engine, the comparison can be made with greater accuracy. If this process results in a match between the facial photo and emotion data and the blacklist, the server immediately sends a generation rejection notification to the device.

[0998] 4. Recheck the generated image

[0999] Once the generated image is complete, the device sends it back to the server and checks it against the database.

[1000] The server activates the facial recognition algorithm and emotion engine to check whether the face and emotional state of the generated image match the registered data. If they match, the generated image is invalidated and notified to the device. If they do not match, the device is notified of "no match" and the image is returned to the user or a third party as valid.

[1001] Specific examples

[1002] 1. Facial photo registration and emotion recognition

[1003] For example, a user can use a dedicated application to upload a photo of their face and register emotional data along with the necessary personal information. The emotion engine detects emotions such as "joy" from the user's photo, and this data is temporarily stored on the device before being sent to the server. At this stage, the facial recognition algorithm works to confirm the validity of the photo.

[1004] 2. Request Generation and Sentiment Matching

[1005] When a third party requests image generation using a face photo registered in the database, the device temporarily stores the face photo and emotion data and sends them to the server. The server then compares this with existing data in the database and sends a notification to the device, such as "This face photo has already been registered."

[1006] 3. Post-generation emotion recognition and face recognition

[1007] After the image is generated using generative AI, it is sent to the server, which checks it against information in its database. If there is no match, the image is returned to the device and the user is notified that it is safe to use.

[1008] Prompt Sentence Examples

[1009] "Please tell me the process for registering a user's face photo and emotion."

[1010] "How do you verify third-party image generation requests?"

[1011] "Please tell us about the process of image authentication and emotion recognition after generation."

[1012] As a result, the present invention can effectively prevent the unauthorized use of facial photographs and emotion data, and ensure the privacy and safety of users.

[1013] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1014] Program processing steps

[1015] Step 1: Enter your photo and personal information

[1016] Users access a dedicated application or website, upload a photo of their face, and enter personal information (such as name and contact details). The emotion engine analyzes the photo and obtains the user's emotional state.

[1017] Input: Face photo, personal information

[1018] Output: Facial photo data, personal information, emotional data

[1019] Specific operation: The user presses the "Upload face photo" button, selects an image file, then enters the necessary information in the "Enter personal information" form and presses the "Submit" button.

[1020] Step 2: Temporarily save data

[1021] The device temporarily stores the facial photo, personal information, and emotional data entered by the user.

[1022] Input: Facial photo data, personal information, emotional data

[1023] Output: Temporarily saved face photo data, personal information, emotional data

[1024] Specific operation: Temporarily saves data in local storage within the application.

[1025] Step 3: Verify the validity of the photo

[1026] The server receives the facial photo sent from the device and verifies its validity using a facial recognition algorithm. If it is valid, the facial photo data, personal information, and emotional data are stored in a database.

[1027] Input: Facial photo data, personal information, emotional data

[1028] Output: Validation result, if valid the data is saved to the database

[1029] Specific operation: The server extracts feature points from the facial photo and analyzes them using a facial recognition algorithm (e.g., OpenCV).

[1030] Step 4: Registration completion notification

[1031] The server notifies the terminal that the validity verification of the face photo has been completed, and the terminal displays a registration completion message to the user.

[1032] Input: Validation result

[1033] Output: Registration completion notification

[1034] Specific operation: The server sends a "Registration complete" notification to the device, and a "Registration complete" pop-up is displayed on the device.

[1035] Step 5: Sending an image generation request

[1036] A user or a third party makes an image generation request, attaches a face photo and emotion data, and sends it to a terminal.

[1037] Input: Image generation request, facial photo data, emotion data

[1038] Output: Face photo data, emotion data, generation request

[1039] Specific operation: Enter the required information in the image generation form and press the "Send generation request" button.

[1040] Step 6: Temporarily save data

[1041] The device temporarily stores the requested facial photo and emotional data.

[1042] Input: face photo data, emotion data, generation request

[1043] Output: Temporarily saved face photo data, emotion data, generation request

[1044] Specific operation: Temporarily save data in the device's memory.

[1045] Step 7: Check against the database

[1046] The server compares the facial photo data and emotion data received from the device with the database, using a facial recognition algorithm and emotion engine to confirm whether the data matches the registered data.

[1047] Input: Facial photo data, emotion data, information in the database

[1048] Output: Matching result (match / mismatch)

[1049] Specific operation: The server analyzes the facial photo and emotional data and compares it with information in a database.

[1050] Step 8: Notification of match results

[1051] The server notifies the terminal of the result of the match. If it matches the registered data, it sends a notification to reject the creation, and if it does not match, it sends a notification to allow the creation.

[1052] Input: Matching result

[1053] Output: Generate approval / rejection notification

[1054] Specific operation: The server communicates the result of "match / no match" to the terminal, and the terminal notifies the user based on the result.

[1055] Step 9: Double-check the generated image

[1056] Once the generated image is complete, the device sends it to the server for another match.

[1057] Input: Generated image

[1058] Output: Verification result (reconfirmation)

[1059] Specific operation: The device sends the generated image to the server and requests reconfirmation.

[1060] Step 10: Notification of the result of matching the generated image

[1061] The server compares the generated image with the information in the database and notifies the terminal of the result of the comparison. If there is a match, the generated image is invalidated; if not, the image is allowed to be used.

[1062] Input: Generated image, information in the database

[1063] Output: Matching result (match / mismatch)

[1064] Specific operation: The server analyzes the generated image, compares it with the database, and notifies the device of the results.

[1065] Step 11: Image Usage Notification

[1066] The device receives notifications from the server and invalidates the generated image if necessary. If there is a notification of no match, the device returns the generated image to the user and notifies them that it is available.

[1067] Input: Verification result (reconfirmation)

[1068] Output: Usage notification / Invalidation notification

[1069] Specific behavior: Display a "Generated images are available" or "Generated images are invalid" message to the user.

[1070] This effectively prevents the unauthorized use of facial photos and emotional data of specific individuals, ensuring the privacy and safety of users.

[1071] (Application example 2)

[1072] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1073] The present invention aims to prevent the misuse of facial photographs of specific individuals in image generation using generative AI, and to further strengthen security by using emotional data. The problem to be solved by this invention is to protect individual privacy and data security, and effectively prevent fraudulent image generation.

[1074] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for registering a facial photograph, means for saving the registered facial photograph in a database, means for comparing the facial photograph with the database when an image generation request is made, means for permitting or rejecting image generation based on the comparison result, means for comparing the generated image with the database again, emotion data recording means for detecting the user's emotional state and recording it together with the facial photograph, means for comparing both the facial photograph and emotion data, and means for notifying the user or a third party based on the comparison result. This reduces the risk of a specific individual's facial photograph being misused, and using emotion data can further strengthen security.

[1075] The "means for registering a face photo" is a function that allows a user to upload a face photo to the system and register it along with personal information.

[1076] The "means for storing registered facial photographs in a database" is a function for safely storing registered facial photographs and related personal information in a database.

[1077] The "means for checking against a database when an image generation request is made" is a function for checking the facial photo data included in the request against registered data in a database when an image generation request is made.

[1078] The "means for permitting or denying image generation based on the result of comparison" is a function for determining whether to permit or deny image generation based on the result of comparison with the database.

[1079] The "means for checking the generated image against the database again" is a function for checking the generated image against the registered data in the database again to confirm safety.

[1080] The "emotion data recording means for detecting the user's emotional state and recording it together with the facial photograph" is a function for detecting the user's emotional state at the same time as registering the facial photograph and recording that data together with the facial photograph.

[1081] "Means for matching both facial photographs and emotional data" is a function that, upon request, matches both facial photographs and emotional data against a database to reduce the risk of fraudulent use.

[1082] The "means for notifying the user or a third party based on the collation result" is a function for notifying the user or a third party of the result when the collation result is a match.

[1083] This invention is a system that aims to prevent specific individuals from misusing their facial photos in AI generation. This system prevents unauthorized use during image generation through a series of processes in which users register their facial photos and record emotional data. The roles of the server, terminal, and user are as follows:

[1084] First, users upload a photo of themselves to a dedicated application or website and provide personal information (such as name and contact details). In addition, an emotion recognition engine detects the user's emotional state, and this data is also recorded. All of this information is temporarily stored on the device and then sent to a server.

[1085] The server uses a facial recognition algorithm and emotional data to verify that the uploaded facial photo is valid and authentic. Once verification is complete, the facial photo and emotional data are stored in a database. Once registration is complete, the server notifies the device, which then displays a message to the user indicating registration is complete.

[1086] Next, when a user or a third party sends an image generation request, the request includes facial photo data and emotion data. The device temporarily saves the request and sends a matching request to the server. The server matches the facial photo and emotion data in the database. If the matching result is a match, it stops image generation and notifies the user or third party of the reason. If there is no match, image generation is allowed.

[1087] After generation, the regenerated image is sent to the server and re-matched using a facial recognition algorithm and emotional data. If the matching facial photo and emotional state match the registered data, the generated image is invalidated and the result is notified to the device. If there is no match, the generated image is returned to the user and their use is permitted.

[1088] For example, when a user registers a face photo, name, and contact information, the system uses a face recognition algorithm to verify whether the face photo is valid. The system also uses an emotion engine to detect the user's emotional state and transmits it to the server. Examples of prompts include:

[1089] Example: "Register a photo of User A to prevent unauthorized use."

[1090] Upload User A.jpg and enter your name, contact information, and emotional state (e.g., happy).

[1091] As described above, the present invention provides a function to prevent unauthorized image generation by using a specific individual's facial photograph and emotional state data, thereby improving the security of personal information and creating an environment where image generation services can be used with peace of mind.

[1092] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1093] Step 1:

[1094] Users access a dedicated application or website and upload a photo of their face. At this time, they also enter personal information (such as name and contact details), and an emotion recognition engine detects the user's emotional state. The entered facial photo data, personal information, and emotional data are temporarily stored on the device.

[1095] Input: Facial photo data, personal information, emotional data

[1096] Output: Facial photo data, personal information, and emotional data temporarily stored on the device

[1097] Step 2:

[1098] The device then sends the stored data to a server, which uses a facial recognition algorithm to verify that the uploaded photo is valid, along with any emotional data.

[1099] Input: Temporarily saved face photo data, personal information, emotional data

[1100] Output: Facial photo data, personal information, and emotional data sent to the server

[1101] Step 3:

[1102] The server stores the received facial photograph and emotion data in a database. After verifying that the facial photograph data and emotion data are valid, the server stores these data in the database.

[1103] Input: Transmitted face photo data, personal information, emotional data

[1104] Output: Facial photo data, personal information, and emotional data stored in a database

[1105] Step 4:

[1106] Once registration is complete, the server notifies the terminal, and the terminal displays a message to the user indicating that registration is complete.

[1107] Input: Facial photo data, personal information, and emotional data stored in the database

[1108] Output: Notification of successful registration, displayed message

[1109] Step 5:

[1110] The user or a third party sends an image generation request, which includes facial photo data and emotion data. The device temporarily stores this data and then sends a matching request to the server.

[1111] Input: Image generation request, facial photo data, emotion data

[1112] Output: Request data temporarily saved on the device, matching request sent

[1113] Step 6:

[1114] The server compares the facial photos in the database with the emotion data and notifies the device of the results. If the comparison results match, the server stops image generation and notifies the device of the reason. If the comparison results do not match, the server allows image generation.

[1115] Input: Matching request sent, face photo data in the database, emotion data

[1116] Output: Matching result, notification of abort if matched, permission to generate if not matched

[1117] Step 7:

[1118] After generation, the regenerated image is sent to the server and re-matched using a facial recognition algorithm and emotional data.

[1119] Input: Generated image data

[1120] Output: Generated image data sent

[1121] Step 8:

[1122] The server compares the generated image data with the database, and if the matching facial photo or emotional state matches the registered data, the generated image is invalidated. The result is notified to the terminal. If there is no match, the generated image is returned to the user.

[1123] Input: Generated image data, face photo data in the database, emotion data

[1124] Output: Matching result, invalidation notification if matched, image returned if not matched

[1125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1127] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1128] [Third embodiment]

[1129] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1133] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1137] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1139] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1140] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1141] This invention relates to a system that prevents specific individuals from misusing their facial photos in AI-generated images. Below, we will explain the specific processing of the system's program and provide details with concrete examples.

[1142] 1. Face photo registration process

[1143] User

[1144] To register their own face photo in the system, users access a dedicated application or website, upload their own face photo, and enter their personal information (name, contact details, etc.).

[1145] Terminal

[1146] The device will temporarily store the uploaded facial photo and use a facial recognition algorithm to verify whether the photo is appropriate. The facial recognition algorithm will analyze the facial features of the uploaded photo to ensure it is a valid facial photo.

[1147] server

[1148] The server stores the user's confirmed facial photo and personal information in a database. After registration is complete, the server notifies the device, and the device displays a message to the user indicating that registration is complete.

[1149] 2. Generate Request Validation Process

[1150] Terminal

[1151] The device receives an image generation request from a user or a third party, temporarily stores the facial photo data included in the request, and then sends a matching request to the server.

[1152] server

[1153] The server receives the matching request and matches the face photo in the request with the face photos stored in its database, using a facial recognition algorithm to compare facial feature points and see if there is a match.

[1154] If the server finds a face photo that matches the blacklist, it notifies the device of the result. If there is no match, it returns a "no match" status to the device.

[1155] 3. Image generation permission / denial process

[1156] Terminal

[1157] The device receives a notification from the server and stops image generation if a face matching the blacklist is found, notifies the user or a third party of the reason for the stop, and terminates the generation process.

[1158] If a "no match" notification is received from the server, the device proceeds with the image generation process.

[1159] 4. Face recognition process after generation

[1160] Terminal

[1161] Once the generated image is complete, the terminal sends it back to the server for verification.

[1162] server

[1163] The server checks the generated image against a database to see if it contains a specific person's face, and if a match is found, invalidates the image and notifies the user of the reason.

[1164] If no match is found, the server notifies the terminal of "no match" and the terminal returns the generated image to the user.

[1165] Specific examples

[1166] 1. Register your face photo

[1167] User "X" uploads a photo of their face via a dedicated app and enters the necessary personal information. The device uses a facial recognition algorithm to verify that the photo is valid, and the server stores it in a database.

[1168] 2. Validating the Generated Request

[1169] A third party "Y" attempts to generate an obscene image based on a specific facial photograph. The device that receives the request sends a matching request to the server, which checks the database and confirms that the facial photograph matches that of user "X." The device rejects the image generation and notifies third party "Y."

[1170] 3. Face recognition after generation

[1171] A third party, "Z," uses a generation AI to generate an image and sends it to the server. The server checks it against a database, finds no match, sends a "no match" notification to the device, and returns the image to the third party, "Z."

[1172] In this way, the present invention effectively prevents specific individuals from being misused by the generation AI, thereby protecting privacy and preventing fraudulent activities.

[1173] The processing flow will be explained below.

[1174] 1. Face photo registration process

[1175] Step 1:

[1176] User

[1177] Users access a dedicated app or website and begin the process of registering their face photo.

[1178] Step 2:

[1179] User

[1180] Users upload a photo of themselves and enter personal information such as their name and contact details.

[1181] Step 3:

[1182] Terminal

[1183] The device temporarily stores the uploaded facial photo.

[1184] Step 4:

[1185] Terminal

[1186] The device uses a facial recognition algorithm to ensure that the submitted photo is valid, analyzing facial feature points and verifying the quality and format.

[1187] Step 5:

[1188] Terminal

[1189] If the facial recognition algorithm determines that the facial photo is valid, the device sends the verification result to the server.

[1190] Step 6:

[1191] server

[1192] The server stores the confirmed facial photograph and the user's personal information in a database.

[1193] Step 7:

[1194] server

[1195] The server notifies the terminal that the registration has been completed and transmits the result to the terminal.

[1196] Step 8:

[1197] Terminal

[1198] The terminal displays a message to the user indicating that registration is complete.

[1199] 2. Generate Request Validation Process

[1200] Step 1:

[1201] User

[1202] A user or a third party sends an image generation request to a terminal.

[1203] Step 2:

[1204] Terminal

[1205] The terminal temporarily stores the facial photo data included in the request.

[1206] Step 3:

[1207] Terminal

[1208] The terminal sends a verification request to the server.

[1209] Step 4:

[1210] server

[1211] The server runs a facial recognition algorithm to match the facial photo data included in the request with a database.

[1212] Step 5:

[1213] server

[1214] The server uses a facial recognition algorithm to compare the facial photo in the request with those in its database to determine if there is a match.

[1215] Step 6:

[1216] server

[1217] If a matching face photo is found, the server sends the result to the device. If not, it sends a "no match" status to the device.

[1218] 3. Image generation permission / denial process

[1219] Step 1:

[1220] Terminal

[1221] The device receives a notification from the server and stops image generation if a matching face photo is included.

[1222] Step 2:

[1223] Terminal

[1224] The terminal notifies the user or a third party of the reason for the cancellation of image generation.

[1225] Step 3:

[1226] Terminal

[1227] If a "no match" notification is received from the server, the terminal begins the image generation process.

[1228] 4. Face recognition process after generation

[1229] Step 1:

[1230] Terminal

[1231] Once the generated image is complete, the device sends it to the server.

[1232] Step 2:

[1233] server

[1234] The server then runs a facial recognition algorithm to match the generated image again against the database.

[1235] Step 3:

[1236] server

[1237] A facial recognition algorithm is used to check whether the face in the generated image matches a face registered in a database.

[1238] Step 4:

[1239] server

[1240] If a matching face is found, the server invalidates the generated image and notifies the device of the result.

[1241] Step 5:

[1242] Terminal

[1243] The terminal notifies the user that the image has been invalidated and deletes the image.

[1244] Step 6:

[1245] server

[1246] On the other hand, if no match is found, the server notifies the terminal that there is no match and returns the generated image to the terminal.

[1247] Step 7:

[1248] Terminal

[1249] The terminal returns the generated image to the user and notifies them that it is available for use.

[1250] Through the above processing steps, it is possible to prevent the user's facial photo from being misused by the generating AI, ensuring privacy and safety.

[1251] Example 1

[1252] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1253] Conventional image generation systems pose a risk that specific individuals may have their facial images misused using generation AI. Furthermore, there is a possibility that the generated images may be tampered with or misused, and there is a lack of means to protect personal privacy and security. This leaves users concerned about the misuse of their facial images. The present invention aims to provide a system that prevents specific individuals from misusing their facial images, allowing them to use image generation services with peace of mind.

[1254] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1255] In this invention, the server includes means for registering facial photos, means for storing the registered facial photos and personal information in a database, means for comparing the photo with the database when an image generation request is made, means for allowing or denying image generation based on the comparison result, means for comparing the generated image with the database again, means for halting image generation if the photo matches the blacklist and notifying the user, and means for comparing the generated image with the database again and returning the generated image if the photo does not match. This makes it possible to prevent the facial photos of specific individuals from being misused by AI generation and to protect privacy safely and effectively.

[1256] "Means for registering face photos" refers to a function that allows users to upload their own face photos to the system through a dedicated application or website.

[1257] "Means for saving to a database" is a function that stores the uploaded facial photo and the user's personal information in a database on the server.

[1258] The "matching means" is a function that, when an image generation request is made, compares the facial photo data included in the request with registered facial photos in the database to confirm whether they match.

[1259] The "means for permitting or denying image generation" is a function that, based on the comparison result, stops image generation if the facial photograph matches the blacklist, and allows image generation to proceed if it does not match.

[1260] The "means for re-matching the generated image" is a function for re-matching the generated image with the database to check whether it matches a facial photograph of a specific person.

[1261] A "blacklist" is a database that manages registered facial photo data and contains a list of facial photos that may be misused in order to prevent fraudulent use.

[1262] The "notification means" is a function that notifies the user or a third party of the reason when a facial photograph matches a blacklist or when a generated image is invalidated.

[1263] The "image generation process" is a series of processes that uses a generative AI model to generate a new image based on input prompt text and image data.

[1264] A "generative AI model" is an artificial intelligence algorithm or framework for generating new images or content based on input data.

[1265] This invention relates to a system that prevents specific individuals from misusing their own facial photos in AI generation. This system reduces the risk of users' facial photo data being used fraudulently and protects their privacy.

[1266] Face photo registration process

[1267] User

[1268] Users access a dedicated application or website and upload a photo of themselves, along with their personal information such as name and contact details, allowing the system to collect all the necessary data in one place.

[1269] Terminal

[1270] The device temporarily stores the face photo uploaded by the user. Then, it uses a facial recognition algorithm (e.g., OpenCV or FaceNet) to verify whether the uploaded face photo is appropriate. Specifically, it analyzes facial feature points (e.g., the positions of the eyes, nose, and mouth) to ensure that the face photo is valid.

[1271] server

[1272] The server saves the verified facial photo and the user's personal information in a database (e.g., MySQL or MongoDB). After saving is complete, the server sends a notification of registration completion to the device, and the device displays a message to the user that registration is complete.

[1273] Generate Request Validation Process

[1274] Terminal

[1275] When a user or a third party sends an image generation request, the device temporarily stores the facial photo data included in the request.The device then sends a matching request to the server to match the facial photo stored in the database with the facial photo included in the request.

[1276] server

[1277] The server receives the matching request and matches the face photo included in the request with the registered face photos in the database. It uses a facial recognition algorithm to compare facial feature points. If the requested face photo matches the blacklist, it notifies the device of the result. If not, it returns a "no match" status to the device.

[1278] Image generation allow / deny process

[1279] Terminal

[1280] The device receives a notification from the server and stops image generation if a matching face is found in the blacklist. It also notifies the user or a third party of the reason for the stop and terminates the generation process. On the other hand, if it receives a notification that there is no match, it continues the image generation process.

[1281] Face recognition process after generation

[1282] Terminal

[1283] After the generated image is completed, the terminal sends the image back to the server for verification.

[1284] server

[1285] The server then compares the generated image against the database to see if it contains the face of a specific person. If a match is found, the server invalidates the generated image and notifies the user of the reason. If a match is not found, the server notifies the user that there is no match, and the device returns the generated image to the user.

[1286] Specific examples

[1287] A specific example of this system is shown below.

[1288] Face photo registration

[1289] User "A" uploads a photo of his face using a dedicated app, enters his name and contact information, and the device validates the photo using a facial recognition algorithm (e.g., OpenCV), and the server stores it in a database.

[1290] Validating the Generated Request

[1291] A third party "B" attempts to generate an image using a generative AI based on a facial photo of user "A." The request is received by the device, and a matching request is sent to the server. The server compares the request with facial photos in its database, and since a match is confirmed, the device rejects the image generation and notifies third party "B."

[1292] Generated face recognition

[1293] A third party "C" generates an image using a generation AI and sends it to the server. The server compares it with the database and returns "no match," and the generated image is returned to third party "C."

[1294] This allows the system to effectively prevent specific individuals from being misused by the generating AI, thereby protecting privacy and preventing fraudulent behavior.

[1295] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1296] Step 1:

[1297] Uploading a user's face photo

[1298] Input: Face photo data, user personal information (name, contact information)

[1299] Output: Temporarily saved face photo data, user personal information

[1300] How it works: The user accesses a dedicated application or website, uploads a photo of their face, and enters their personal information. The device then temporarily stores this data.

[1301] Step 2:

[1302] Verifying the validity of your photo

[1303] Input: Temporarily saved face photo data

[1304] Output: Validation result

[1305] How it works: The device uses a facial recognition algorithm (such as OpenCV or FaceNet) to analyze the features of the uploaded face photo and check whether it is a valid face photo. It detects feature points such as the positions of the eyes, nose, and mouth to confirm validity.

[1306] Step 3:

[1307] Saving to a database

[1308] Input: Verified face photo data, user personal information

[1309] Output: Face photo and personal information stored in the database

[1310] Specific operation: The server saves the verified face photo data and the user's personal information in the database. Once the saving is complete, the server sends a registration completion notification to the device, and the device displays a registration completion message to the user.

[1311] Step 4:

[1312] Receiving and temporarily saving image generation requests

[1313] Input: Image generation request, facial photo data included in the request

[1314] Output: Temporarily saved face photo data

[1315] Specific operation: When an image generation request is sent from a user or a third party, the terminal receives the request and temporarily stores the facial photo data included in the request.

[1316] Step 5:

[1317] Database matching request

[1318] Input: Temporarily saved face photo data

[1319] Output: Matching request

[1320] Specific operation: The terminal sends a matching request to the server to request that the facial photo stored in the database be matched with the facial photo included in the request.

[1321] Step 6:

[1322] Face photo matching process

[1323] Input: Matching request, face photo data stored in the database

[1324] Output: Match result (match or no match)

[1325] Specific operation: The server compares the facial photo data stored in the database with the facial photo data included in the matching request. It uses a facial recognition algorithm to compare facial features. If the requested facial photo matches the blacklist, it notifies the device of the result. If it does not match, it sends a "no match" status.

[1326] Step 7:

[1327] Image Generation Permission or Cancellation Notice

[1328] Input: Matching result

[1329] Output: Image generation approval or rejection notification

[1330] Specific operation: The device receives a notification from the server, and if a face matching the blacklist is found, it stops image generation. It also notifies the user or a third party of the reason and terminates the generation process. If it receives a "no match" notification, it continues the image generation process.

[1331] Step 8:

[1332] Verifying the generated image

[1333] Input: Generated image

[1334] Output: Verification result (valid or invalid), final output image

[1335] Specific operation: Once an image is generated, the device sends it back to the server, which then compares it with the database. It checks to see if it contains the face of a specific person, and if a match is found, it invalidates the generated image and notifies the user of the reason. If there is no match, the server notifies the user that there is no match and returns the generated image to the user.

[1336] (Application example 1)

[1337] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1338] In today's world, many individuals upload their facial photos to social media and other online platforms, increasing the risk that these photos will be misused by generative AI. Such misuse infringes on individuals' privacy and sometimes results in defamation. Furthermore, existing security systems lack automation for the registration and verification process of facial photos, and therefore are unable to provide fast and efficient protection. Therefore, there is a need for the development of a reliable, real-time system to prevent the misuse of individuals' facial photos by generative AI.

[1339] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1340] In this invention, the server includes means for registering a facial photo, means for saving the registered facial photo in a database, means for comparing the facial photo with the database when an image generation request is made, means for permitting or rejecting image generation based on the comparison result, means for comparing the generated image with the database again, means for registering and verifying the facial photo using an application installed on the smartphone, and means for transmitting the facial photo and verification result to the server in real time. This effectively prevents the misuse of individuals' facial photos, making it possible to protect privacy and suppress fraudulent activities.

[1341] A "means for registering a facial photo" is a device or software that allows a user to upload a facial photo to the system and temporarily store the photo within the system.

[1342] The "means for storing in a database" refers to a device or program that has the function of safely and efficiently storing registered facial photographs and related personal information in a database.

[1343] "Means for matching with a database when requesting image generation" refers to a system with a comparison function that checks whether the requested facial photo matches a photo already registered in the database before the generation AI generates the image.

[1344] The "means for permitting or denying image generation based on the matching result" refers to a system that has the function of executing a process that denies image generation if the facial photograph matches as a result of the matching, and permits image generation if the facial photograph does not match.

[1345] "Means for comparing the generated image with the database again" refers to a system that has a process for checking whether the face contained in the generated image matches a facial photograph in the database, and if so, invalidating the generated image.

[1346] "Means for registering and verifying facial photographs using an application installed on a smartphone" refers to a system that allows users to register facial photographs using a dedicated application that runs on a smartphone, and has the function of verifying whether the photograph is valid.

[1347] The "means for transmitting facial photographs and verification results to a server in real time" refers to a system that has a communication protocol for instantly transmitting facial photographs and verification results to a server via a smartphone application and storing them in a database on the server side.

[1348] This invention is a system for preventing specific individuals from misusing their facial photos in AI generation. This system consists of a smartphone application and a server system. This system operates as follows.

[1349] First, a user registers a face photo using a dedicated application installed on their smartphone. The user then takes a photo of their face or selects an existing photo and enters their personal information (name, contact details, etc.). The application then runs a facial recognition algorithm (using the OpenCV library, for example) to verify that the photo is valid. Once verified, the face photo and personal information are sent to a server in real time and stored in a database.

[1350] Next, when a request for image generation is made using the generation AI, the device receives the request and temporarily stores the facial photo included in the request.The device then sends the facial photo data to the server, which then compares it with registered facial photos in its database.If the comparison results in a match, the server sends a notification to the device rejecting image generation, and the device notifies the user or a third party of this.If there is a mismatch, the device allows image generation.

[1351] The generated image also undergoes the same facial recognition process. The device sends the generated image to the server, which then checks it against the database again. If a match is found, the image is invalidated and the user is notified. If a match is not found, the generated image is returned to the user.

[1352] Consider the following scenario: A user completes the facial photo registration process, and then a third party attempts to generate a pornographic image. However, the server-side verification finds that the image matches the user's facial photo, and the attempt is rejected. The generated image is also verified by the server, and if a match is found, the image is invalidated and the user is notified.

[1353] Examples of prompts that demonstrate the operation of this system include the following:

[1354] "Please verify that this photo matches a registered person in our database. If we suspect abuse, we will stop the generation and notify you."

[1355] As a result, the present invention effectively prevents personal facial photos from being misused by AI generation, thereby protecting privacy and preventing fraudulent activities.

[1356] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1357] Step 1:

[1358] A user uses a smartphone application to take or select a face photo and enter personal information (such as name and contact details). The application then temporarily stores the entered face photo and personal information on the device.

[1359] Input: User's photo, name, contact information

[1360] Data processing: taking or selecting a photo of your face, entering personal information

[1361] Output: Temporarily saved face photo and personal information

[1362] Step 2:

[1363] The device uses a facial recognition algorithm (such as the OpenCV library) to verify whether the registered face photo is valid. The facial recognition algorithm analyzes facial feature points to determine whether the photo is valid.

[1364] Input: Temporarily saved face photo

[1365] Data calculation: Apply face recognition algorithm to analyze facial feature points

[1366] Output: Judgment result of whether the face photo is valid or not

[1367] Step 3:

[1368] The device sends the facial photo and personal information that are determined to be valid to the server, which then stores this information in a database.

[1369] Input: Valid face photo and personal information

[1370] Data processing: Send face photo and personal information to the server

[1371] Output: Face photo and personal information stored in the database

[1372] Step 4:

[1373] The device receives an image generation request using the generative AI model, temporarily stores the facial photo data included in the generation request, and then transmits the facial photo data to the server.

[1374] Input: Image generation request, facial photo data included in the generation request

[1375] Data processing: Temporarily save face photo data and send it to the server

[1376] Output: Facial photo data sent to the server

[1377] Step 5:

[1378] The server matches the submitted facial photo data with those stored in its database, using a facial recognition algorithm to compare facial features and determine whether there is a match.

[1379] Input: Requested face photo data, face photos in the database

[1380] Data calculation: Apply face recognition algorithm and compare feature points

[1381] Output: Match result (match or no match)

[1382] Step 6:

[1383] Based on the result of the comparison, the server notifies the terminal whether to allow or deny image generation. If the comparison results are a match, image generation is denied, and if they are not a match, image generation is allowed.

[1384] Input: Matching result

[1385] Data Processing: Generating Accept or Deny Notifications

[1386] Output: Notification to device (allow or deny)

[1387] Step 7:

[1388] The terminal receives the notification from the server and notifies the user or a third party based on the result. If image generation is rejected, the terminal notifies the user of the reason and stops image generation.

[1389] Input: Notification from the server (allow or deny)

[1390] Data processing: generating notifications for users

[1391] Output: Notification to the user or third party

[1392] Step 8:

[1393] Once the generated image is complete, the device sends it back to the server, which checks it against the database and compares facial features again to determine if there is a match.

[1394] Input: Generated image

[1395] Data calculation: Apply face recognition algorithm and compare feature points

[1396] Output: Match result (match or no match)

[1397] Step 9:

[1398] The server validates or invalidates the generated image based on the results of the second check. If the facial photo matches, the image is invalidated and the device is notified of this. If there is a mismatch, the generated image is retained.

[1399] Input: Matching result

[1400] Data processing: Activation or deactivation processing, notification generation

[1401] Output: Notifications to the device (enabled or disabled)

[1402] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1403] This invention combines a system that prevents specific individuals from misusing their facial photos for AI generation with an emotion engine that recognizes the user's emotions. Below, we will explain the specific processing of the system's program and provide details with concrete examples.

[1404] 1. Face photo registration and emotion recognition process

[1405] User

[1406] Users access a dedicated application or website to register their facial photo with the system. They upload their facial photo and enter personal information (such as name and contact details). At the same time, the emotion engine detects and records the user's emotional state.

[1407] Terminal

[1408] The device temporarily stores the uploaded facial photo, and also records the emotion data detected by the emotion engine.

[1409] server

[1410] The server uses a facial recognition algorithm to verify that the uploaded facial photo is valid. After verification, the facial photo and emotional data are stored in a database. After registration is complete, the server notifies the device, and the device displays a message to the user indicating registration is complete.

[1411] 2. Request Generation and Sentiment Matching Process

[1412] Terminal

[1413] A user or a third party sends an image generation request to a device. Not only the facial photo data included in the request, but also the emotion data is temporarily stored for comparison.

[1414] server

[1415] A matching request is sent to the server, and the facial photo data and emotion data are matched against the database. The facial recognition algorithm compares facial feature points, and the emotion engine matches the emotional state.

[1416] server

[1417] If the facial photo and emotion data match the blacklist, the server notifies the device. If they do not match, it returns a "no match" status to the device.

[1418] 3. Image generation permission / denial process

[1419] Terminal

[1420] The device receives a notification from the server and, if a matching facial photo or emotional state is found, stops image generation. It notifies the user or a third party of the reason for the stop. If a "no match" notification is received, the device starts the image generation process.

[1421] 4. Post-generation emotion recognition and face recognition process

[1422] Terminal

[1423] Once the generated image is complete, it is sent to the server and checked again.

[1424] server

[1425] The server runs a facial recognition algorithm and emotion engine to match the submitted generated image against a database to see if the face and emotional state in the generated image match the registered face.

[1426] server

[1427] If a matching face or specific emotional state is detected, the server invalidates the generated image and notifies the device of the result. If no match is found, the server notifies the device of "no match" and returns the generated image.

[1428] Terminal

[1429] The terminal receives the notification from the server and invalidates the generated image if necessary, or returns the generated image to the user if there is no problem with the image and notifies the user that it is available for use.

[1430] Specific examples

[1431] 1. Facial photo registration and emotion recognition

[1432] User "A" uploads a photo of his / her face using a dedicated app and registers emotional data along with necessary personal information. The device verifies the data using a facial recognition algorithm and emotion engine, and the server stores it in a database.

[1433] 2. Request Generation and Sentiment Matching

[1434] A third party "B" sends a request to generate an image using a facial photo. The device temporarily stores the facial photo and emotion information and sends a matching request to the server. The server verifies the photo using a facial recognition algorithm and emotion engine, and if the information matches that of user "A," it notifies the device that generation is rejected.

[1435] 3. Post-generation emotion recognition and face recognition

[1436] After the third party "C" generates an image using the generation AI, it sends it to the server. The server then uses a facial recognition algorithm and emotion engine to check the image again, and if there is no match, it notifies the device. The device then returns the image to the third party "C."

[1437] In this way, the present invention effectively prevents a specific individual's facial photograph and emotional state from being misused by the generation AI, ensuring privacy and safety.

[1438] The processing flow will be explained below.

[1439] 1. Face photo registration and emotion recognition process

[1440] Step 1:

[1441] User

[1442] Users access a dedicated app or website and begin the process of registering their face photo.

[1443] Step 2:

[1444] User

[1445] Users upload a photo of themselves and enter personal information such as their name and contact details.

[1446] Step 3:

[1447] Terminal

[1448] The device will temporarily store the uploaded facial photo.

[1449] Step 4:

[1450] Terminal

[1451] An emotion engine on the device detects the user's emotional state during upload, such as whether the user is feeling a particular emotion, such as relief, surprise, anger, or sadness.

[1452] Step 5:

[1453] Terminal

[1454] Emotion data detected by the emotion engine is also recorded.

[1455] Step 6:

[1456] server

[1457] The server uses a facial recognition algorithm to verify that the uploaded facial photo is valid.

[1458] Step 7:

[1459] server

[1460] After confirmation, the server stores the facial photo, emotional data, and personal information in a database.

[1461] Step 8:

[1462] server

[1463] The server sends a notification of registration completion to the terminal, and the terminal displays a completion message to the user.

[1464] 2. Request Generation and Sentiment Matching Process

[1465] Step 1:

[1466] User

[1467] A user or third party submits an image generation request.

[1468] Step 2:

[1469] Terminal

[1470] The facial photo data and emotion data included in the request are temporarily stored.

[1471] Step 3:

[1472] Terminal

[1473] The device sends a matching request to the server, including a facial photo and emotion data.

[1474] Step 4:

[1475] server

[1476] The server runs a facial recognition algorithm and matches the face photo in the request with those in its database.

[1477] Step 5:

[1478] server

[1479] The emotion engine is used to match the emotion data of the request with the emotion data in the database.

[1480] Step 6:

[1481] server

[1482] It uses facial recognition algorithms and emotion engines to determine whether there is a match with the database.

[1483] Step 7:

[1484] server

[1485] If a matching face photo or emotion data is found, the server notifies the device of the result. If no match is found, a "no match" status is sent to the device.

[1486] 3. Image generation permission / denial process

[1487] Step 1:

[1488] Terminal

[1489] The device receives a notification from the server and stops image generation if a matching facial photo or emotional state is found.

[1490] Step 2:

[1491] Terminal

[1492] The terminal notifies the user or a third party of the reason for the cancellation of image generation.

[1493] Step 3:

[1494] Terminal

[1495] If a "no match" notification is received from the server, the image generation process begins.

[1496] 4. Post-generation emotion recognition and face recognition process

[1497] Step 1:

[1498] Terminal

[1499] Once the generated image is complete, it is sent to the server and verified again.

[1500] Step 2:

[1501] server

[1502] The server then runs a facial recognition algorithm and emotion engine to match the generated image against the database again.

[1503] Step 3:

[1504] server

[1505] A facial recognition algorithm is used to check whether the face in the generated image matches a face registered in a database.

[1506] Step 4:

[1507] server

[1508] An emotion engine is used to check whether the face in the generated image is in a particular emotional state.

[1509] Step 5:

[1510] server

[1511] If a matching face or a specific emotional state is detected, the server invalidates the generated image and notifies the device of the result.

[1512] Step 6:

[1513] Terminal

[1514] The terminal notifies the user that the image has been invalidated and deletes the image.

[1515] Step 7:

[1516] server

[1517] If the verification does not confirm a match, the server notifies the terminal that there is no match and returns the generated image to the terminal.

[1518] Step 8:

[1519] Terminal

[1520] The terminal returns the generated image to the user and notifies them that it is available for use.

[1521] Through the above processing steps, the present invention can prevent both the user's facial photo and emotional state from being misused by the generating AI, thereby ensuring privacy and safety.

[1522] Example 2

[1523] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1524] When preventing a specific individual from misusing their own facial photograph, if the validity of the registered facial photograph and the emotional data are not sufficiently verified, the image may be generated fraudulently. As a result, the risk of violating the user's privacy and emotional state increases. There is a need for technology that solves this problem and prevents the fraudulent use of facial photographs and emotional data.

[1525] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1526] In this invention, the server includes: means for a user to upload their own facial photo and input personal information and emotion data; means for the terminal to temporarily store the uploaded facial photo and detected emotion data; means for the server to verify the validity of the facial photo using a facial recognition algorithm and store the facial photo and emotion data in a database; means for a user or a third party to send an image generation request and temporarily store the facial photo and emotion data included in the request; means for the server to compare the sent facial photo and emotion data with the database and allow or deny image generation based on the comparison result; and means for resending the generated image and for the server to compare it using a facial recognition algorithm and emotion engine. This effectively prevents the fraudulent use of the facial photo and emotion data and ensures the user's privacy and safety.

[1527] "User" refers to an entity that uses the system to register their own facial photograph and make an image generation request.

[1528] A "terminal" is a device that allows a user to access the system and has the function of temporarily storing facial photographs and emotional data.

[1529] "Server" refers to the central computer system that verifies, stores, and matches facial photos and emotional data.

[1530] A "face photo" refers to an image of a specific individual's face, and is used for personal authentication and emotion recognition.

[1531] "Personal Information" refers to personal data such as a user's name, contact details, etc.

[1532] "Emotion data" refers to data that indicates the user's emotional state detected by the emotion engine from a facial photograph.

[1533] A "facial recognition algorithm" refers to a computational method for extracting feature points from a facial photograph and identifying a specific individual.

[1534] An "emotion engine" refers to a system that recognizes a user's emotions from a facial photograph and records that information as data.

[1535] A "database" refers to a system for systematically storing and managing facial photographs, personal information, emotional data, etc.

[1536] "Image generation request" refers to a request sent by a user or third party to the system to generate a new image based on a facial photograph.

[1537] A "blacklist" refers to a restricted list used to prevent unauthorized use of registered facial photos and emotional data.

[1538] The present invention is a system for preventing specific individuals from having their facial photographs misused. This system controls and manages a series of processes, from the stage where a user uploads their facial photograph and registers personal information and emotional data, to the stage where the generated image is reconfirmed to ensure its appropriateness.

[1539] 1. The process for users to register their face photos

[1540] Users access a dedicated application or website and upload a photo of their face. When uploading, they enter personal information (name, contact details, etc.) as well as emotion data that the emotion engine will detect. This information is temporarily stored on the device at the initial stage.

[1541] The emotion engine can be, for example, the Emotion API from Microsoft Azure Cognitive Services, which detects emotions from uploaded images and records them as data.

[1542] 2. Storing facial photos and emotion data in a database

[1543] The device temporarily stores the received facial photo and emotion data and sends them to the server.

[1544] The server uses a facial recognition algorithm (e.g., OpenCV library) to verify that the face photo is valid. After verification is complete, the face photo and emotion data are stored in a database. This ensures that the user's face photo and emotion data are managed securely.

[1545] 3. Processing a creation request

[1546] When a user or a third party sends an image generation request to a device, the request includes a facial photo and emotion data, which are temporarily stored on the device and later sent to a server.

[1547] The server compares the received facial photo and emotion data with the data in its database. Using a facial recognition algorithm and emotion engine, the comparison can be made with greater accuracy. If this process results in a match between the facial photo and emotion data and the blacklist, the server immediately sends a generation rejection notification to the device.

[1548] 4. Recheck the generated image

[1549] Once the generated image is complete, the device sends it back to the server and checks it against the database.

[1550] The server activates the facial recognition algorithm and emotion engine to check whether the face and emotional state of the generated image match the registered data. If they match, the generated image is invalidated and notified to the device. If they do not match, the device is notified of "no match" and the image is returned to the user or a third party as valid.

[1551] Specific examples

[1552] 1. Facial photo registration and emotion recognition

[1553] For example, a user can use a dedicated application to upload a photo of their face and register emotional data along with the necessary personal information. The emotion engine detects emotions such as "joy" from the user's photo, and this data is temporarily stored on the device before being sent to the server. At this stage, the facial recognition algorithm works to confirm the validity of the photo.

[1554] 2. Request Generation and Sentiment Matching

[1555] When a third party requests image generation using a face photo registered in the database, the device temporarily stores the face photo and emotion data and sends them to the server. The server then compares this with existing data in the database and sends a notification to the device, such as "This face photo has already been registered."

[1556] 3. Post-generation emotion recognition and face recognition

[1557] After the image is generated using generative AI, it is sent to the server, which checks it against information in its database. If there is no match, the image is returned to the device and the user is notified that it is safe to use.

[1558] Prompt Sentence Examples

[1559] "Please tell me the process for registering a user's face photo and emotion."

[1560] "How do you verify third-party image generation requests?"

[1561] "Please tell us about the process of image authentication and emotion recognition after generation."

[1562] As a result, the present invention can effectively prevent the unauthorized use of facial photographs and emotion data, and ensure the privacy and safety of users.

[1563] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1564] Program processing steps

[1565] Step 1: Enter your photo and personal information

[1566] Users access a dedicated application or website, upload a photo of their face, and enter personal information (such as name and contact details). The emotion engine analyzes the photo and obtains the user's emotional state.

[1567] Input: Face photo, personal information

[1568] Output: Facial photo data, personal information, emotional data

[1569] Specific operation: The user presses the "Upload face photo" button, selects an image file, then enters the necessary information in the "Enter personal information" form and presses the "Submit" button.

[1570] Step 2: Temporarily save data

[1571] The device temporarily stores the facial photo, personal information, and emotional data entered by the user.

[1572] Input: Facial photo data, personal information, emotional data

[1573] Output: Temporarily saved face photo data, personal information, emotional data

[1574] Specific operation: Temporarily saves data in local storage within the application.

[1575] Step 3: Verify the validity of the photo

[1576] The server receives the facial photo sent from the device and verifies its validity using a facial recognition algorithm. If it is valid, the facial photo data, personal information, and emotional data are stored in a database.

[1577] Input: Facial photo data, personal information, emotional data

[1578] Output: Validation result, if valid the data is saved to the database

[1579] Specific operation: The server extracts feature points from the facial photo and analyzes them using a facial recognition algorithm (e.g., OpenCV).

[1580] Step 4: Registration completion notification

[1581] The server notifies the terminal that the validity verification of the face photo has been completed, and the terminal displays a registration completion message to the user.

[1582] Input: Validation result

[1583] Output: Registration completion notification

[1584] Specific operation: The server sends a "Registration complete" notification to the device, and a "Registration complete" pop-up is displayed on the device.

[1585] Step 5: Sending an image generation request

[1586] A user or a third party makes an image generation request, attaches a face photo and emotion data, and sends it to a terminal.

[1587] Input: Image generation request, facial photo data, emotion data

[1588] Output: Face photo data, emotion data, generation request

[1589] Specific operation: Enter the required information in the image generation form and press the "Send generation request" button.

[1590] Step 6: Temporarily save data

[1591] The device temporarily stores the requested facial photo and emotional data.

[1592] Input: face photo data, emotion data, generation request

[1593] Output: Temporarily saved face photo data, emotion data, generation request

[1594] Specific operation: Temporarily save data in the device's memory.

[1595] Step 7: Check against the database

[1596] The server compares the facial photo data and emotion data received from the device with the database, using a facial recognition algorithm and emotion engine to confirm whether the data matches the registered data.

[1597] Input: Facial photo data, emotion data, information in the database

[1598] Output: Matching result (match / mismatch)

[1599] Specific operation: The server analyzes the facial photo and emotional data and compares it with information in a database.

[1600] Step 8: Notification of match results

[1601] The server notifies the terminal of the result of the match. If it matches the registered data, it sends a notification to reject the creation, and if it does not match, it sends a notification to allow the creation.

[1602] Input: Matching result

[1603] Output: Generate approval / rejection notification

[1604] Specific operation: The server communicates the result of "match / no match" to the terminal, and the terminal notifies the user based on the result.

[1605] Step 9: Double-check the generated image

[1606] Once the generated image is complete, the device sends it to the server for another match.

[1607] Input: Generated image

[1608] Output: Verification result (reconfirmation)

[1609] Specific operation: The device sends the generated image to the server and requests reconfirmation.

[1610] Step 10: Notification of the result of matching the generated image

[1611] The server compares the generated image with the information in the database and notifies the terminal of the result of the comparison. If there is a match, the generated image is invalidated; if not, the image is allowed to be used.

[1612] Input: Generated image, information in the database

[1613] Output: Matching result (match / mismatch)

[1614] Specific operation: The server analyzes the generated image, compares it with the database, and notifies the device of the results.

[1615] Step 11: Image Usage Notification

[1616] The device receives notifications from the server and invalidates the generated image if necessary. If there is a notification of no match, the device returns the generated image to the user and notifies them that it is available.

[1617] Input: Verification result (reconfirmation)

[1618] Output: Usage notification / Invalidation notification

[1619] Specific behavior: Display a "Generated images are available" or "Generated images are invalid" message to the user.

[1620] This effectively prevents the unauthorized use of facial photos and emotional data of specific individuals, ensuring the privacy and safety of users.

[1621] (Application example 2)

[1622] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1623] The present invention aims to prevent the misuse of facial photographs of specific individuals in image generation using generative AI, and to further strengthen security by using emotional data. The problem to be solved by this invention is to protect individual privacy and data security, and effectively prevent fraudulent image generation.

[1624] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for registering a facial photograph, means for saving the registered facial photograph in a database, means for comparing the facial photograph with the database when an image generation request is made, means for permitting or rejecting image generation based on the comparison result, means for comparing the generated image with the database again, emotion data recording means for detecting the user's emotional state and recording it together with the facial photograph, means for comparing both the facial photograph and emotion data, and means for notifying the user or a third party based on the comparison result. This reduces the risk of a specific individual's facial photograph being misused, and using emotion data can further strengthen security.

[1625] The "means for registering a face photo" is a function that allows a user to upload a face photo to the system and register it along with personal information.

[1626] The "means for storing registered facial photographs in a database" is a function for safely storing registered facial photographs and related personal information in a database.

[1627] The "means for checking against a database when an image generation request is made" is a function for checking the facial photo data included in the request against registered data in a database when an image generation request is made.

[1628] The "means for permitting or denying image generation based on the result of comparison" is a function for determining whether to permit or deny image generation based on the result of comparison with the database.

[1629] The "means for checking the generated image against the database again" is a function for checking the generated image against the registered data in the database again to confirm safety.

[1630] The "emotion data recording means for detecting the user's emotional state and recording it together with the facial photograph" is a function for detecting the user's emotional state at the same time as registering the facial photograph and recording that data together with the facial photograph.

[1631] "Means for matching both facial photographs and emotional data" is a function that, upon request, matches both facial photographs and emotional data against a database to reduce the risk of fraudulent use.

[1632] The "means for notifying the user or a third party based on the collation result" is a function for notifying the user or a third party of the result when the collation result is a match.

[1633] This invention is a system that aims to prevent specific individuals from misusing their facial photos in AI generation. This system prevents unauthorized use during image generation through a series of processes in which users register their facial photos and record emotional data. The roles of the server, terminal, and user are as follows:

[1634] First, users upload a photo of themselves to a dedicated application or website and provide personal information (such as name and contact details). In addition, an emotion recognition engine detects the user's emotional state, and this data is also recorded. All of this information is temporarily stored on the device and then sent to a server.

[1635] The server uses a facial recognition algorithm and emotional data to verify that the uploaded facial photo is valid and authentic. Once verification is complete, the facial photo and emotional data are stored in a database. Once registration is complete, the server notifies the device, which then displays a message to the user indicating registration is complete.

[1636] Next, when a user or a third party sends an image generation request, the request includes facial photo data and emotion data. The device temporarily saves the request and sends a matching request to the server. The server matches the facial photo and emotion data in the database. If the matching result is a match, it stops image generation and notifies the user or third party of the reason. If there is no match, image generation is allowed.

[1637] After generation, the regenerated image is sent to the server and re-matched using a facial recognition algorithm and emotional data. If the matching facial photo and emotional state match the registered data, the generated image is invalidated and the result is notified to the device. If there is no match, the generated image is returned to the user and their use is permitted.

[1638] For example, when a user registers a face photo, name, and contact information, the system uses a face recognition algorithm to verify whether the face photo is valid. The system also uses an emotion engine to detect the user's emotional state and transmits it to the server. Examples of prompts include:

[1639] Example: "Register a photo of User A to prevent unauthorized use."

[1640] Upload User A.jpg and enter your name, contact information, and emotional state (e.g., happy).

[1641] As described above, the present invention provides a function to prevent unauthorized image generation by using a specific individual's facial photograph and emotional state data, thereby improving the security of personal information and creating an environment where image generation services can be used with peace of mind.

[1642] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1643] Step 1:

[1644] Users access a dedicated application or website and upload a photo of their face. At this time, they also enter personal information (such as name and contact details), and an emotion recognition engine detects the user's emotional state. The entered facial photo data, personal information, and emotional data are temporarily stored on the device.

[1645] Input: Facial photo data, personal information, emotional data

[1646] Output: Facial photo data, personal information, and emotional data temporarily stored on the device

[1647] Step 2:

[1648] The device then sends the stored data to a server, which uses a facial recognition algorithm to verify that the uploaded photo is valid, along with any emotional data.

[1649] Input: Temporarily saved face photo data, personal information, emotional data

[1650] Output: Facial photo data, personal information, and emotional data sent to the server

[1651] Step 3:

[1652] The server stores the received facial photograph and emotion data in a database. After verifying that the facial photograph data and emotion data are valid, the server stores these data in the database.

[1653] Input: Transmitted face photo data, personal information, emotional data

[1654] Output: Facial photo data, personal information, and emotional data stored in a database

[1655] Step 4:

[1656] Once registration is complete, the server notifies the terminal, and the terminal displays a message to the user indicating that registration is complete.

[1657] Input: Facial photo data, personal information, and emotional data stored in the database

[1658] Output: Notification of successful registration, displayed message

[1659] Step 5:

[1660] The user or a third party sends an image generation request, which includes facial photo data and emotion data. The device temporarily stores this data and then sends a matching request to the server.

[1661] Input: Image generation request, facial photo data, emotion data

[1662] Output: Request data temporarily saved on the device, matching request sent

[1663] Step 6:

[1664] The server compares the facial photos in the database with the emotion data and notifies the device of the results. If the comparison results match, the server stops image generation and notifies the device of the reason. If the comparison results do not match, the server allows image generation.

[1665] Input: Matching request sent, face photo data in the database, emotion data

[1666] Output: Matching result, notification of abort if matched, permission to generate if not matched

[1667] Step 7:

[1668] After generation, the regenerated image is sent to the server and re-matched using a facial recognition algorithm and emotional data.

[1669] Input: Generated image data

[1670] Output: Generated image data sent

[1671] Step 8:

[1672] The server compares the generated image data with the database, and if the matching facial photo or emotional state matches the registered data, the generated image is invalidated. The result is notified to the terminal. If there is no match, the generated image is returned to the user.

[1673] Input: Generated image data, face photo data in the database, emotion data

[1674] Output: Matching result, invalidation notification if matched, image returned if not matched

[1675] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1676] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1677] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1678] [Fourth embodiment]

[1679] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1680] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1681] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1682] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1683] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1684] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1685] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1686] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1687] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1688] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1689] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1690] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1691] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1692] This invention relates to a system that prevents specific individuals from misusing their facial photos in AI-generated images. Below, we will explain the specific processing of the system's program and provide details with concrete examples.

[1693] 1. Face photo registration process

[1694] User

[1695] To register their own face photo in the system, users access a dedicated application or website, upload their own face photo, and enter their personal information (name, contact details, etc.).

[1696] Terminal

[1697] The device will temporarily store the uploaded facial photo and use a facial recognition algorithm to verify whether the photo is appropriate. The facial recognition algorithm will analyze the facial features of the uploaded photo to ensure it is a valid facial photo.

[1698] server

[1699] The server stores the user's confirmed facial photo and personal information in a database. After registration is complete, the server notifies the device, and the device displays a message to the user indicating that registration is complete.

[1700] 2. Generate Request Validation Process

[1701] Terminal

[1702] The device receives an image generation request from a user or a third party, temporarily stores the facial photo data included in the request, and then sends a matching request to the server.

[1703] server

[1704] The server receives the matching request and matches the face photo in the request with the face photos stored in its database, using a facial recognition algorithm to compare facial feature points and see if there is a match.

[1705] If the server finds a face photo that matches the blacklist, it notifies the device of the result. If there is no match, it returns a "no match" status to the device.

[1706] 3. Image generation permission / denial process

[1707] Terminal

[1708] The device receives a notification from the server and stops image generation if a face matching the blacklist is found, notifies the user or a third party of the reason for the stop, and terminates the generation process.

[1709] If a "no match" notification is received from the server, the device proceeds with the image generation process.

[1710] 4. Face recognition process after generation

[1711] Terminal

[1712] Once the generated image is complete, the terminal sends it back to the server for verification.

[1713] server

[1714] The server checks the generated image against a database to see if it contains a specific person's face, and if a match is found, invalidates the image and notifies the user of the reason.

[1715] If no match is found, the server notifies the terminal of "no match" and the terminal returns the generated image to the user.

[1716] Specific examples

[1717] 1. Register your face photo

[1718] User "X" uploads a photo of their face via a dedicated app and enters the necessary personal information. The device uses a facial recognition algorithm to verify that the photo is valid, and the server stores it in a database.

[1719] 2. Validating the Generated Request

[1720] A third party "Y" attempts to generate an obscene image based on a specific facial photograph. The device that receives the request sends a matching request to the server, which checks the database and confirms that the facial photograph matches that of user "X." The device rejects the image generation and notifies third party "Y."

[1721] 3. Face recognition after generation

[1722] A third party, "Z," uses a generation AI to generate an image and sends it to the server. The server checks it against a database, finds no match, sends a "no match" notification to the device, and returns the image to the third party, "Z."

[1723] In this way, the present invention effectively prevents specific individuals from being misused by the generation AI, thereby protecting privacy and preventing fraudulent activities.

[1724] The processing flow will be explained below.

[1725] 1. Face photo registration process

[1726] Step 1:

[1727] User

[1728] Users access a dedicated app or website and begin the process of registering their face photo.

[1729] Step 2:

[1730] User

[1731] Users upload a photo of themselves and enter personal information such as their name and contact details.

[1732] Step 3:

[1733] Terminal

[1734] The device temporarily stores the uploaded facial photo.

[1735] Step 4:

[1736] Terminal

[1737] The device uses a facial recognition algorithm to ensure that the submitted photo is valid, analyzing facial feature points and verifying the quality and format.

[1738] Step 5:

[1739] Terminal

[1740] If the facial recognition algorithm determines that the facial photo is valid, the device sends the verification result to the server.

[1741] Step 6:

[1742] server

[1743] The server stores the confirmed facial photograph and the user's personal information in a database.

[1744] Step 7:

[1745] server

[1746] The server notifies the terminal that the registration has been completed and transmits the result to the terminal.

[1747] Step 8:

[1748] Terminal

[1749] The terminal displays a message to the user indicating that registration is complete.

[1750] 2. Generate Request Validation Process

[1751] Step 1:

[1752] User

[1753] A user or a third party sends an image generation request to a terminal.

[1754] Step 2:

[1755] Terminal

[1756] The terminal temporarily stores the facial photo data included in the request.

[1757] Step 3:

[1758] Terminal

[1759] The terminal sends a verification request to the server.

[1760] Step 4:

[1761] server

[1762] The server runs a facial recognition algorithm to match the facial photo data included in the request with a database.

[1763] Step 5:

[1764] server

[1765] The server uses a facial recognition algorithm to compare the facial photo in the request with those in its database to determine if there is a match.

[1766] Step 6:

[1767] server

[1768] If a matching face photo is found, the server sends the result to the device. If not, it sends a "no match" status to the device.

[1769] 3. Image generation permission / denial process

[1770] Step 1:

[1771] Terminal

[1772] The device receives a notification from the server and stops image generation if a matching face photo is included.

[1773] Step 2:

[1774] Terminal

[1775] The terminal notifies the user or a third party of the reason for the cancellation of image generation.

[1776] Step 3:

[1777] Terminal

[1778] If a "no match" notification is received from the server, the terminal begins the image generation process.

[1779] 4. Face recognition process after generation

[1780] Step 1:

[1781] Terminal

[1782] Once the generated image is complete, the device sends it to the server.

[1783] Step 2:

[1784] server

[1785] The server then runs a facial recognition algorithm to match the generated image again against the database.

[1786] Step 3:

[1787] server

[1788] A facial recognition algorithm is used to check whether the face in the generated image matches a face registered in a database.

[1789] Step 4:

[1790] server

[1791] If a matching face is found, the server invalidates the generated image and notifies the device of the result.

[1792] Step 5:

[1793] Terminal

[1794] The terminal notifies the user that the image has been invalidated and deletes the image.

[1795] Step 6:

[1796] server

[1797] On the other hand, if no match is found, the server notifies the terminal that there is no match and returns the generated image to the terminal.

[1798] Step 7:

[1799] Terminal

[1800] The terminal returns the generated image to the user and notifies them that it is available for use.

[1801] Through the above processing steps, it is possible to prevent the user's facial photo from being misused by the generating AI, ensuring privacy and safety.

[1802] Example 1

[1803] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1804] Conventional image generation systems pose a risk that specific individuals may have their facial images misused using generation AI. Furthermore, there is a possibility that the generated images may be tampered with or misused, and there is a lack of means to protect personal privacy and security. This leaves users concerned about the misuse of their facial images. The present invention aims to provide a system that prevents specific individuals from misusing their facial images, allowing them to use image generation services with peace of mind.

[1805] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1806] In this invention, the server includes means for registering facial photos, means for storing the registered facial photos and personal information in a database, means for comparing the photo with the database when an image generation request is made, means for allowing or denying image generation based on the comparison result, means for comparing the generated image with the database again, means for halting image generation if the photo matches the blacklist and notifying the user, and means for comparing the generated image with the database again and returning the generated image if the photo does not match. This makes it possible to prevent the facial photos of specific individuals from being misused by AI generation and to protect privacy safely and effectively.

[1807] "Means for registering face photos" refers to a function that allows users to upload their own face photos to the system through a dedicated application or website.

[1808] "Means for saving to a database" is a function that stores the uploaded facial photo and the user's personal information in a database on the server.

[1809] The "matching means" is a function that, when an image generation request is made, compares the facial photo data included in the request with registered facial photos in the database to confirm whether they match.

[1810] The "means for permitting or denying image generation" is a function that, based on the comparison result, stops image generation if the facial photograph matches the blacklist, and allows image generation to proceed if it does not match.

[1811] The "means for re-matching the generated image" is a function for re-matching the generated image with the database to check whether it matches a facial photograph of a specific person.

[1812] A "blacklist" is a database that manages registered facial photo data and contains a list of facial photos that may be misused in order to prevent fraudulent use.

[1813] The "notification means" is a function that notifies the user or a third party of the reason when a facial photograph matches a blacklist or when a generated image is invalidated.

[1814] The "image generation process" is a series of processes that uses a generative AI model to generate a new image based on input prompt text and image data.

[1815] A "generative AI model" is an artificial intelligence algorithm or framework for generating new images or content based on input data.

[1816] This invention relates to a system that prevents specific individuals from misusing their own facial photos in AI generation. This system reduces the risk of users' facial photo data being used fraudulently and protects their privacy.

[1817] Face photo registration process

[1818] User

[1819] Users access a dedicated application or website and upload a photo of themselves, along with their personal information such as name and contact details, allowing the system to collect all the necessary data in one place.

[1820] Terminal

[1821] The device temporarily stores the face photo uploaded by the user. Then, it uses a facial recognition algorithm (e.g., OpenCV or FaceNet) to verify whether the uploaded face photo is appropriate. Specifically, it analyzes facial feature points (e.g., the positions of the eyes, nose, and mouth) to ensure that the face photo is valid.

[1822] server

[1823] The server saves the verified facial photo and the user's personal information in a database (e.g., MySQL or MongoDB). After saving is complete, the server sends a notification of registration completion to the device, and the device displays a message to the user that registration is complete.

[1824] Generate Request Validation Process

[1825] Terminal

[1826] When a user or a third party sends an image generation request, the device temporarily stores the facial photo data included in the request.The device then sends a matching request to the server to match the facial photo stored in the database with the facial photo included in the request.

[1827] server

[1828] The server receives the matching request and matches the face photo included in the request with the registered face photos in the database. It uses a facial recognition algorithm to compare facial feature points. If the requested face photo matches the blacklist, it notifies the device of the result. If not, it returns a "no match" status to the device.

[1829] Image generation allow / deny process

[1830] Terminal

[1831] The device receives a notification from the server and stops image generation if a matching face is found in the blacklist. It also notifies the user or a third party of the reason for the stop and terminates the generation process. On the other hand, if it receives a notification that there is no match, it continues the image generation process.

[1832] Face recognition process after generation

[1833] Terminal

[1834] After the generated image is completed, the terminal sends the image back to the server for verification.

[1835] server

[1836] The server then compares the generated image against the database to see if it contains the face of a specific person. If a match is found, the server invalidates the generated image and notifies the user of the reason. If a match is not found, the server notifies the user that there is no match, and the device returns the generated image to the user.

[1837] Specific examples

[1838] A specific example of this system is shown below.

[1839] Face photo registration

[1840] User "A" uploads a photo of his face using a dedicated app, enters his name and contact information, and the device validates the photo using a facial recognition algorithm (e.g., OpenCV), and the server stores it in a database.

[1841] Validating the Generated Request

[1842] A third party "B" attempts to generate an image using a generative AI based on a facial photo of user "A." The request is received by the device, and a matching request is sent to the server. The server compares the request with facial photos in its database, and since a match is confirmed, the device rejects the image generation and notifies third party "B."

[1843] Generated face recognition

[1844] A third party "C" generates an image using a generation AI and sends it to the server. The server compares it with the database and returns "no match," and the generated image is returned to third party "C."

[1845] This allows the system to effectively prevent specific individuals from being misused by the generating AI, thereby protecting privacy and preventing fraudulent behavior.

[1846] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1847] Step 1:

[1848] Uploading a user's face photo

[1849] Input: Face photo data, user personal information (name, contact information)

[1850] Output: Temporarily saved face photo data, user personal information

[1851] How it works: The user accesses a dedicated application or website, uploads a photo of their face, and enters their personal information. The device then temporarily stores this data.

[1852] Step 2:

[1853] Verifying the validity of your photo

[1854] Input: Temporarily saved face photo data

[1855] Output: Validation result

[1856] How it works: The device uses a facial recognition algorithm (such as OpenCV or FaceNet) to analyze the features of the uploaded face photo and check whether it is a valid face photo. It detects feature points such as the positions of the eyes, nose, and mouth to confirm validity.

[1857] Step 3:

[1858] Saving to a database

[1859] Input: Verified face photo data, user personal information

[1860] Output: Face photo and personal information stored in the database

[1861] Specific operation: The server saves the verified face photo data and the user's personal information in the database. Once the saving is complete, the server sends a registration completion notification to the device, and the device displays a registration completion message to the user.

[1862] Step 4:

[1863] Receiving and temporarily saving image generation requests

[1864] Input: Image generation request, facial photo data included in the request

[1865] Output: Temporarily saved face photo data

[1866] Specific operation: When an image generation request is sent from a user or a third party, the terminal receives the request and temporarily stores the facial photo data included in the request.

[1867] Step 5:

[1868] Database matching request

[1869] Input: Temporarily saved face photo data

[1870] Output: Matching request

[1871] Specific operation: The terminal sends a matching request to the server to request that the facial photo stored in the database be matched with the facial photo included in the request.

[1872] Step 6:

[1873] Face photo matching process

[1874] Input: Matching request, face photo data stored in the database

[1875] Output: Match result (match or no match)

[1876] Specific operation: The server compares the facial photo data stored in the database with the facial photo data included in the matching request. It uses a facial recognition algorithm to compare facial features. If the requested facial photo matches the blacklist, it notifies the device of the result. If it does not match, it sends a "no match" status.

[1877] Step 7:

[1878] Image Generation Permission or Cancellation Notice

[1879] Input: Matching result

[1880] Output: Image generation approval or rejection notification

[1881] Specific operation: The device receives a notification from the server, and if a face matching the blacklist is found, it stops image generation. It also notifies the user or a third party of the reason and terminates the generation process. If it receives a "no match" notification, it continues the image generation process.

[1882] Step 8:

[1883] Verifying the generated image

[1884] Input: Generated image

[1885] Output: Verification result (valid or invalid), final output image

[1886] Specific operation: Once an image is generated, the device sends it back to the server, which then compares it with the database. It checks to see if it contains the face of a specific person, and if a match is found, it invalidates the generated image and notifies the user of the reason. If there is no match, the server notifies the user that there is no match and returns the generated image to the user.

[1887] (Application example 1)

[1888] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1889] In today's world, many individuals upload their facial photos to social media and other online platforms, increasing the risk that these photos will be misused by generative AI. Such misuse infringes on individuals' privacy and sometimes results in defamation. Furthermore, existing security systems lack automation for the registration and verification process of facial photos, and therefore are unable to provide fast and efficient protection. Therefore, there is a need for the development of a reliable, real-time system to prevent the misuse of individuals' facial photos by generative AI.

[1890] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1891] In this invention, the server includes means for registering a facial photo, means for saving the registered facial photo in a database, means for comparing the facial photo with the database when an image generation request is made, means for permitting or rejecting image generation based on the comparison result, means for comparing the generated image with the database again, means for registering and verifying the facial photo using an application installed on the smartphone, and means for transmitting the facial photo and verification result to the server in real time. This effectively prevents the misuse of individuals' facial photos, making it possible to protect privacy and suppress fraudulent activities.

[1892] A "means for registering a facial photo" is a device or software that allows a user to upload a facial photo to the system and temporarily store the photo within the system.

[1893] The "means for storing in a database" refers to a device or program that has the function of safely and efficiently storing registered facial photographs and related personal information in a database.

[1894] "Means for matching with a database when requesting image generation" refers to a system with a comparison function that checks whether the requested facial photo matches a photo already registered in the database before the generation AI generates the image.

[1895] The "means for permitting or denying image generation based on the matching result" refers to a system that has the function of executing a process that denies image generation if the facial photograph matches as a result of the matching, and permits image generation if the facial photograph does not match.

[1896] "Means for comparing the generated image with the database again" refers to a system that has a process for checking whether the face contained in the generated image matches a facial photograph in the database, and if so, invalidating the generated image.

[1897] "Means for registering and verifying facial photographs using an application installed on a smartphone" refers to a system that allows users to register facial photographs using a dedicated application that runs on a smartphone, and has the function of verifying whether the photograph is valid.

[1898] The "means for transmitting facial photographs and verification results to a server in real time" refers to a system that has a communication protocol for instantly transmitting facial photographs and verification results to a server via a smartphone application and storing them in a database on the server side.

[1899] This invention is a system for preventing specific individuals from misusing their facial photos in AI generation. This system consists of a smartphone application and a server system. This system operates as follows.

[1900] First, a user registers a face photo using a dedicated application installed on their smartphone. The user then takes a photo of their face or selects an existing photo and enters their personal information (name, contact details, etc.). The application then runs a facial recognition algorithm (using the OpenCV library, for example) to verify that the photo is valid. Once verified, the face photo and personal information are sent to a server in real time and stored in a database.

[1901] Next, when a request for image generation is made using the generation AI, the device receives the request and temporarily stores the facial photo included in the request.The device then sends the facial photo data to the server, which then compares it with registered facial photos in its database.If the comparison results in a match, the server sends a notification to the device rejecting image generation, and the device notifies the user or a third party of this.If there is a mismatch, the device allows image generation.

[1902] The generated image also undergoes the same facial recognition process. The device sends the generated image to the server, which then checks it against the database again. If a match is found, the image is invalidated and the user is notified. If a match is not found, the generated image is returned to the user.

[1903] Consider the following scenario: A user completes the facial photo registration process, and then a third party attempts to generate a pornographic image. However, the server-side verification finds that the image matches the user's facial photo, and the attempt is rejected. The generated image is also verified by the server, and if a match is found, the image is invalidated and the user is notified.

[1904] Examples of prompts that demonstrate the operation of this system include the following:

[1905] "Please verify that this photo matches a registered person in our database. If we suspect abuse, we will stop the generation and notify you."

[1906] As a result, the present invention effectively prevents personal facial photos from being misused by AI generation, thereby protecting privacy and preventing fraudulent activities.

[1907] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1908] Step 1:

[1909] A user uses a smartphone application to take or select a face photo and enter personal information (such as name and contact details). The application then temporarily stores the entered face photo and personal information on the device.

[1910] Input: User's photo, name, contact information

[1911] Data processing: taking or selecting a photo of your face, entering personal information

[1912] Output: Temporarily saved face photo and personal information

[1913] Step 2:

[1914] The device uses a facial recognition algorithm (such as the OpenCV library) to verify whether the registered face photo is valid. The facial recognition algorithm analyzes facial feature points to determine whether the photo is valid.

[1915] Input: Temporarily saved face photo

[1916] Data calculation: Apply face recognition algorithm to analyze facial feature points

[1917] Output: Judgment result of whether the face photo is valid or not

[1918] Step 3:

[1919] The device sends the facial photo and personal information that are determined to be valid to the server, which then stores this information in a database.

[1920] Input: Valid face photo and personal information

[1921] Data processing: Send face photo and personal information to the server

[1922] Output: Face photo and personal information stored in the database

[1923] Step 4:

[1924] The device receives an image generation request using the generative AI model, temporarily stores the facial photo data included in the generation request, and then transmits the facial photo data to the server.

[1925] Input: Image generation request, facial photo data included in the generation request

[1926] Data processing: Temporarily save face photo data and send it to the server

[1927] Output: Facial photo data sent to the server

[1928] Step 5:

[1929] The server matches the submitted facial photo data with those stored in its database, using a facial recognition algorithm to compare facial features and determine whether there is a match.

[1930] Input: Requested face photo data, face photos in the database

[1931] Data calculation: Apply face recognition algorithm and compare feature points

[1932] Output: Match result (match or no match)

[1933] Step 6:

[1934] Based on the result of the comparison, the server notifies the terminal whether to allow or deny image generation. If the comparison results are a match, image generation is denied, and if they are not a match, image generation is allowed.

[1935] Input: Matching result

[1936] Data Processing: Generating Accept or Deny Notifications

[1937] Output: Notification to device (allow or deny)

[1938] Step 7:

[1939] The terminal receives the notification from the server and notifies the user or a third party based on the result. If image generation is rejected, the terminal notifies the user of the reason and stops image generation.

[1940] Input: Notification from the server (allow or deny)

[1941] Data processing: generating notifications for users

[1942] Output: Notification to the user or third party

[1943] Step 8:

[1944] Once the generated image is complete, the device sends it back to the server, which checks it against the database and compares facial features again to determine if there is a match.

[1945] Input: Generated image

[1946] Data calculation: Apply face recognition algorithm and compare feature points

[1947] Output: Match result (match or no match)

[1948] Step 9:

[1949] The server validates or invalidates the generated image based on the results of the second check. If the facial photo matches, the image is invalidated and the device is notified of this. If there is a mismatch, the generated image is retained.

[1950] Input: Matching result

[1951] Data processing: Activation or deactivation processing, notification generation

[1952] Output: Notifications to the device (enabled or disabled)

[1953] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1954] This invention combines a system that prevents specific individuals from misusing their facial photos for AI generation with an emotion engine that recognizes the user's emotions. Below, we will explain the specific processing of the system's program and provide details with concrete examples.

[1955] 1. Face photo registration and emotion recognition process

[1956] User

[1957] Users access a dedicated application or website to register their facial photo with the system. They upload their facial photo and enter personal information (such as name and contact details). At the same time, the emotion engine detects and records the user's emotional state.

[1958] Terminal

[1959] The device temporarily stores the uploaded facial photo, and also records the emotion data detected by the emotion engine.

[1960] server

[1961] The server uses a facial recognition algorithm to verify that the uploaded facial photo is valid. After verification, the facial photo and emotional data are stored in a database. After registration is complete, the server notifies the device, and the device displays a message to the user indicating registration is complete.

[1962] 2. Request Generation and Sentiment Matching Process

[1963] Terminal

[1964] A user or a third party sends an image generation request to a device. Not only the facial photo data included in the request, but also the emotion data is temporarily stored for comparison.

[1965] server

[1966] A matching request is sent to the server, and the facial photo data and emotion data are matched against the database. The facial recognition algorithm compares facial feature points, and the emotion engine matches the emotional state.

[1967] server

[1968] If the facial photo and emotion data match the blacklist, the server notifies the device. If they do not match, it returns a "no match" status to the device.

[1969] 3. Image generation permission / denial process

[1970] Terminal

[1971] The device receives a notification from the server and, if a matching facial photo or emotional state is found, stops image generation. It notifies the user or a third party of the reason for the stop. If a "no match" notification is received, the device starts the image generation process.

[1972] 4. Post-generation emotion recognition and face recognition process

[1973] Terminal

[1974] Once the generated image is complete, it is sent to the server and checked again.

[1975] server

[1976] The server runs a facial recognition algorithm and emotion engine to match the submitted generated image against a database to see if the face and emotional state in the generated image match the registered face.

[1977] server

[1978] If a matching face or specific emotional state is detected, the server invalidates the generated image and notifies the device of the result. If no match is found, the server notifies the device of "no match" and returns the generated image.

[1979] Terminal

[1980] The terminal receives the notification from the server and invalidates the generated image if necessary, or returns the generated image to the user if there is no problem with the image and notifies the user that it is available for use.

[1981] Specific examples

[1982] 1. Facial photo registration and emotion recognition

[1983] User "A" uploads a photo of his / her face using a dedicated app and registers emotional data along with necessary personal information. The device verifies the data using a facial recognition algorithm and emotion engine, and the server stores it in a database.

[1984] 2. Request Generation and Sentiment Matching

[1985] A third party "B" sends a request to generate an image using a facial photo. The device temporarily stores the facial photo and emotion information and sends a matching request to the server. The server verifies the photo using a facial recognition algorithm and emotion engine, and if the information matches that of user "A," it notifies the device that generation is rejected.

[1986] 3. Post-generation emotion recognition and face recognition

[1987] After the third party "C" generates an image using the generation AI, it sends it to the server. The server then uses a facial recognition algorithm and emotion engine to check the image again, and if there is no match, it notifies the device. The device then returns the image to the third party "C."

[1988] In this way, the present invention effectively prevents a specific individual's facial photograph and emotional state from being misused by the generation AI, ensuring privacy and safety.

[1989] The processing flow will be explained below.

[1990] 1. Face photo registration and emotion recognition process

[1991] Step 1:

[1992] User

[1993] Users access a dedicated app or website and begin the process of registering their face photo.

[1994] Step 2:

[1995] User

[1996] Users upload a photo of themselves and enter personal information such as their name and contact details.

[1997] Step 3:

[1998] Terminal

[1999] The device will temporarily store the uploaded facial photo.

[2000] Step 4:

[2001] Terminal

[2002] An emotion engine on the device detects the user's emotional state during upload, such as whether the user is feeling a particular emotion, such as relief, surprise, anger, or sadness.

[2003] Step 5:

[2004] Terminal

[2005] Emotion data detected by the emotion engine is also recorded.

[2006] Step 6:

[2007] server

[2008] The server uses a facial recognition algorithm to verify that the uploaded facial photo is valid.

[2009] Step 7:

[2010] server

[2011] After confirmation, the server stores the facial photo, emotional data, and personal information in a database.

[2012] Step 8:

[2013] server

[2014] The server sends a notification of registration completion to the terminal, and the terminal displays a completion message to the user.

[2015] 2. Request Generation and Sentiment Matching Process

[2016] Step 1:

[2017] User

[2018] A user or third party submits an image generation request.

[2019] Step 2:

[2020] Terminal

[2021] The facial photo data and emotion data included in the request are temporarily stored.

[2022] Step 3:

[2023] Terminal

[2024] The device sends a matching request to the server, including a facial photo and emotion data.

[2025] Step 4:

[2026] server

[2027] The server runs a facial recognition algorithm and matches the face photo in the request with those in its database.

[2028] Step 5:

[2029] server

[2030] The emotion engine is used to match the emotion data of the request with the emotion data in the database.

[2031] Step 6:

[2032] server

[2033] It uses facial recognition algorithms and emotion engines to determine whether there is a match with the database.

[2034] Step 7:

[2035] server

[2036] If a matching face photo or emotion data is found, the server notifies the device of the result. If no match is found, a "no match" status is sent to the device.

[2037] 3. Image generation permission / denial process

[2038] Step 1:

[2039] Terminal

[2040] The device receives a notification from the server and stops image generation if a matching facial photo or emotional state is found.

[2041] Step 2:

[2042] Terminal

[2043] The terminal notifies the user or a third party of the reason for the cancellation of image generation.

[2044] Step 3:

[2045] Terminal

[2046] If a "no match" notification is received from the server, the image generation process begins.

[2047] 4. Post-generation emotion recognition and face recognition process

[2048] Step 1:

[2049] Terminal

[2050] Once the generated image is complete, it is sent to the server and verified again.

[2051] Step 2:

[2052] server

[2053] The server then runs a facial recognition algorithm and emotion engine to match the generated image against the database again.

[2054] Step 3:

[2055] server

[2056] A facial recognition algorithm is used to check whether the face in the generated image matches a face registered in a database.

[2057] Step 4:

[2058] server

[2059] An emotion engine is used to check whether the face in the generated image is in a particular emotional state.

[2060] Step 5:

[2061] server

[2062] If a matching face or a specific emotional state is detected, the server invalidates the generated image and notifies the device of the result.

[2063] Step 6:

[2064] Terminal

[2065] The terminal notifies the user that the image has been invalidated and deletes the image.

[2066] Step 7:

[2067] server

[2068] If the verification does not confirm a match, the server notifies the terminal that there is no match and returns the generated image to the terminal.

[2069] Step 8:

[2070] Terminal

[2071] The terminal returns the generated image to the user and notifies them that it is available for use.

[2072] Through the above processing steps, the present invention can prevent both the user's facial photo and emotional state from being misused by the generating AI, thereby ensuring privacy and safety.

[2073] Example 2

[2074] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2075] When preventing a specific individual from misusing their own facial photograph, if the validity of the registered facial photograph and the emotional data are not sufficiently verified, the image may be generated fraudulently. As a result, the risk of violating the user's privacy and emotional state increases. There is a need for technology that solves this problem and prevents the fraudulent use of facial photographs and emotional data.

[2076] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2077] In this invention, the server includes: means for a user to upload their own facial photo and input personal information and emotion data; means for the terminal to temporarily store the uploaded facial photo and detected emotion data; means for the server to verify the validity of the facial photo using a facial recognition algorithm and store the facial photo and emotion data in a database; means for a user or a third party to send an image generation request and temporarily store the facial photo and emotion data included in the request; means for the server to compare the sent facial photo and emotion data with the database and allow or deny image generation based on the comparison result; and means for resending the generated image and for the server to compare it using a facial recognition algorithm and emotion engine. This effectively prevents the fraudulent use of the facial photo and emotion data and ensures the user's privacy and safety.

[2078] "User" refers to an entity that uses the system to register their own facial photograph and make an image generation request.

[2079] A "terminal" is a device that allows a user to access the system and has the function of temporarily storing facial photographs and emotional data.

[2080] "Server" refers to the central computer system that verifies, stores, and matches facial photos and emotional data.

[2081] A "face photo" refers to an image of a specific individual's face, and is used for personal authentication and emotion recognition.

[2082] "Personal Information" refers to personal data such as a user's name, contact details, etc.

[2083] "Emotion data" refers to data that indicates the user's emotional state detected by the emotion engine from a facial photograph.

[2084] A "facial recognition algorithm" refers to a computational method for extracting feature points from a facial photograph and identifying a specific individual.

[2085] An "emotion engine" refers to a system that recognizes a user's emotions from a facial photograph and records that information as data.

[2086] A "database" refers to a system for systematically storing and managing facial photographs, personal information, emotional data, etc.

[2087] "Image generation request" refers to a request sent by a user or third party to the system to generate a new image based on a facial photograph.

[2088] A "blacklist" refers to a restricted list used to prevent unauthorized use of registered facial photos and emotional data.

[2089] The present invention is a system for preventing specific individuals from having their facial photographs misused. This system controls and manages a series of processes, from the stage where a user uploads their facial photograph and registers personal information and emotional data, to the stage where the generated image is reconfirmed to ensure its appropriateness.

[2090] 1. The process for users to register their face photos

[2091] Users access a dedicated application or website and upload a photo of their face. When uploading, they enter personal information (name, contact details, etc.) as well as emotion data that the emotion engine will detect. This information is temporarily stored on the device at the initial stage.

[2092] The emotion engine can be, for example, the Emotion API from Microsoft Azure Cognitive Services, which detects emotions from uploaded images and records them as data.

[2093] 2. Storing facial photos and emotion data in a database

[2094] The device temporarily stores the received facial photo and emotion data and sends them to the server.

[2095] The server uses a facial recognition algorithm (e.g., OpenCV library) to verify that the face photo is valid. After verification is complete, the face photo and emotion data are stored in a database. This ensures that the user's face photo and emotion data are managed securely.

[2096] 3. Processing a creation request

[2097] When a user or a third party sends an image generation request to a device, the request includes a facial photo and emotion data, which are temporarily stored on the device and later sent to a server.

[2098] The server compares the received facial photo and emotion data with the data in its database. Using a facial recognition algorithm and emotion engine, the comparison can be made with greater accuracy. If this process results in a match between the facial photo and emotion data and the blacklist, the server immediately sends a generation rejection notification to the device.

[2099] 4. Recheck the generated image

[2100] Once the generated image is complete, the device sends it back to the server and checks it against the database.

[2101] The server activates the facial recognition algorithm and emotion engine to check whether the face and emotional state of the generated image match the registered data. If they match, the generated image is invalidated and notified to the device. If they do not match, the device is notified of "no match" and the image is returned to the user or a third party as valid.

[2102] Specific examples

[2103] 1. Facial photo registration and emotion recognition

[2104] For example, a user can use a dedicated application to upload a photo of their face and register emotional data along with the necessary personal information. The emotion engine detects emotions such as "joy" from the user's photo, and this data is temporarily stored on the device before being sent to the server. At this stage, the facial recognition algorithm works to confirm the validity of the photo.

[2105] 2. Request Generation and Sentiment Matching

[2106] When a third party requests image generation using a face photo registered in the database, the device temporarily stores the face photo and emotion data and sends them to the server. The server then compares this with existing data in the database and sends a notification to the device, such as "This face photo has already been registered."

[2107] 3. Post-generation emotion recognition and face recognition

[2108] After the image is generated using generative AI, it is sent to the server, which checks it against information in its database. If there is no match, the image is returned to the device and the user is notified that it is safe to use.

[2109] Prompt Sentence Examples

[2110] "Please tell me the process for registering a user's face photo and emotion."

[2111] "How do you verify third-party image generation requests?"

[2112] "Please tell us about the process of image authentication and emotion recognition after generation."

[2113] As a result, the present invention can effectively prevent the unauthorized use of facial photographs and emotion data, and ensure the privacy and safety of users.

[2114] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2115] Program processing steps

[2116] Step 1: Enter your photo and personal information

[2117] Users access a dedicated application or website, upload a photo of their face, and enter personal information (such as name and contact details). The emotion engine analyzes the photo and obtains the user's emotional state.

[2118] Input: Face photo, personal information

[2119] Output: Facial photo data, personal information, emotional data

[2120] Specific operation: The user presses the "Upload face photo" button, selects an image file, then enters the necessary information in the "Enter personal information" form and presses the "Submit" button.

[2121] Step 2: Temporarily save data

[2122] The device temporarily stores the facial photo, personal information, and emotional data entered by the user.

[2123] Input: Facial photo data, personal information, emotional data

[2124] Output: Temporarily saved face photo data, personal information, emotional data

[2125] Specific operation: Temporarily saves data in local storage within the application.

[2126] Step 3: Verify the validity of the photo

[2127] The server receives the facial photo sent from the device and verifies its validity using a facial recognition algorithm. If it is valid, the facial photo data, personal information, and emotional data are stored in a database.

[2128] Input: Facial photo data, personal information, emotional data

[2129] Output: Validation result, if valid the data is saved to the database

[2130] Specific operation: The server extracts feature points from the facial photo and analyzes them using a facial recognition algorithm (e.g., OpenCV).

[2131] Step 4: Registration completion notification

[2132] The server notifies the terminal that the validity verification of the face photo has been completed, and the terminal displays a registration completion message to the user.

[2133] Input: Validation result

[2134] Output: Registration completion notification

[2135] Specific operation: The server sends a "Registration complete" notification to the device, and a "Registration complete" pop-up is displayed on the device.

[2136] Step 5: Sending an image generation request

[2137] A user or a third party makes an image generation request, attaches a face photo and emotion data, and sends it to a terminal.

[2138] Input: Image generation request, facial photo data, emotion data

[2139] Output: Face photo data, emotion data, generation request

[2140] Specific operation: Enter the required information in the image generation form and press the "Send generation request" button.

[2141] Step 6: Temporarily save data

[2142] The device temporarily stores the requested facial photo and emotional data.

[2143] Input: face photo data, emotion data, generation request

[2144] Output: Temporarily saved face photo data, emotion data, generation request

[2145] Specific operation: Temporarily save data in the device's memory.

[2146] Step 7: Check against the database

[2147] The server compares the facial photo data and emotion data received from the device with the database, using a facial recognition algorithm and emotion engine to confirm whether the data matches the registered data.

[2148] Input: Facial photo data, emotion data, information in the database

[2149] Output: Matching result (match / mismatch)

[2150] Specific operation: The server analyzes the facial photo and emotional data and compares it with information in a database.

[2151] Step 8: Notification of match results

[2152] The server notifies the terminal of the result of the match. If it matches the registered data, it sends a notification to reject the creation, and if it does not match, it sends a notification to allow the creation.

[2153] Input: Matching result

[2154] Output: Generate approval / rejection notification

[2155] Specific operation: The server communicates the result of "match / no match" to the terminal, and the terminal notifies the user based on the result.

[2156] Step 9: Double-check the generated image

[2157] Once the generated image is complete, the device sends it to the server for another match.

[2158] Input: Generated image

[2159] Output: Verification result (reconfirmation)

[2160] Specific operation: The device sends the generated image to the server and requests reconfirmation.

[2161] Step 10: Notification of the result of matching the generated image

[2162] The server compares the generated image with the information in the database and notifies the terminal of the result of the comparison. If there is a match, the generated image is invalidated; if not, the image is allowed to be used.

[2163] Input: Generated image, information in the database

[2164] Output: Matching result (match / mismatch)

[2165] Specific operation: The server analyzes the generated image, compares it with the database, and notifies the device of the results.

[2166] Step 11: Image Usage Notification

[2167] The device receives notifications from the server and invalidates the generated image if necessary. If there is a notification of no match, the device returns the generated image to the user and notifies them that it is available.

[2168] Input: Verification result (reconfirmation)

[2169] Output: Usage notification / Invalidation notification

[2170] Specific behavior: Display a "Generated images are available" or "Generated images are invalid" message to the user.

[2171] This effectively prevents the unauthorized use of facial photos and emotional data of specific individuals, ensuring the privacy and safety of users.

[2172] (Application example 2)

[2173] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2174] The present invention aims to prevent the misuse of facial photographs of specific individuals in image generation using generative AI, and to further strengthen security by using emotional data. The problem to be solved by this invention is to protect individual privacy and data security, and effectively prevent fraudulent image generation.

[2175] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for registering a facial photograph, means for saving the registered facial photograph in a database, means for comparing the facial photograph with the database when an image generation request is made, means for permitting or rejecting image generation based on the comparison result, means for comparing the generated image with the database again, emotion data recording means for detecting the user's emotional state and recording it together with the facial photograph, means for comparing both the facial photograph and emotion data, and means for notifying the user or a third party based on the comparison result. This reduces the risk of a specific individual's facial photograph being misused, and using emotion data can further strengthen security.

[2176] The "means for registering a face photo" is a function that allows a user to upload a face photo to the system and register it along with personal information.

[2177] The "means for storing registered facial photographs in a database" is a function for safely storing registered facial photographs and related personal information in a database.

[2178] The "means for checking against a database when an image generation request is made" is a function for checking the facial photo data included in the request against registered data in a database when an image generation request is made.

[2179] The "means for permitting or denying image generation based on the result of comparison" is a function for determining whether to permit or deny image generation based on the result of comparison with the database.

[2180] The "means for checking the generated image against the database again" is a function for checking the generated image against the registered data in the database again to confirm safety.

[2181] The "emotion data recording means for detecting the user's emotional state and recording it together with the facial photograph" is a function for detecting the user's emotional state at the same time as registering the facial photograph and recording that data together with the facial photograph.

[2182] "Means for matching both facial photographs and emotional data" is a function that, upon request, matches both facial photographs and emotional data against a database to reduce the risk of fraudulent use.

[2183] The "means for notifying the user or a third party based on the collation result" is a function for notifying the user or a third party of the result when the collation result is a match.

[2184] This invention is a system that aims to prevent specific individuals from misusing their facial photos in AI generation. This system prevents unauthorized use during image generation through a series of processes in which users register their facial photos and record emotional data. The roles of the server, terminal, and user are as follows:

[2185] First, users upload a photo of themselves to a dedicated application or website and provide personal information (such as name and contact details). In addition, an emotion recognition engine detects the user's emotional state, and this data is also recorded. All of this information is temporarily stored on the device and then sent to a server.

[2186] The server uses a facial recognition algorithm and emotional data to verify that the uploaded facial photo is valid and authentic. Once verification is complete, the facial photo and emotional data are stored in a database. Once registration is complete, the server notifies the device, which then displays a message to the user indicating registration is complete.

[2187] Next, when a user or a third party sends an image generation request, the request includes facial photo data and emotion data. The device temporarily saves the request and sends a matching request to the server. The server matches the facial photo and emotion data in the database. If the matching result is a match, it stops image generation and notifies the user or third party of the reason. If there is no match, image generation is allowed.

[2188] After generation, the regenerated image is sent to the server and re-matched using a facial recognition algorithm and emotional data. If the matching facial photo and emotional state match the registered data, the generated image is invalidated and the result is notified to the device. If there is no match, the generated image is returned to the user and their use is permitted.

[2189] For example, when a user registers a face photo, name, and contact information, the system uses a face recognition algorithm to verify whether the face photo is valid. The system also uses an emotion engine to detect the user's emotional state and transmits it to the server. Examples of prompts include:

[2190] Example: "Register a photo of User A to prevent unauthorized use."

[2191] Upload User A.jpg and enter your name, contact information, and emotional state (e.g., happy).

[2192] As described above, the present invention provides a function to prevent unauthorized image generation by using a specific individual's facial photograph and emotional state data, thereby improving the security of personal information and creating an environment where image generation services can be used with peace of mind.

[2193] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2194] Step 1:

[2195] Users access a dedicated application or website and upload a photo of their face. At this time, they also enter personal information (such as name and contact details), and an emotion recognition engine detects the user's emotional state. The entered facial photo data, personal information, and emotional data are temporarily stored on the device.

[2196] Input: Facial photo data, personal information, emotional data

[2197] Output: Facial photo data, personal information, and emotional data temporarily stored on the device

[2198] Step 2:

[2199] The device then sends the stored data to a server, which uses a facial recognition algorithm to verify that the uploaded photo is valid, along with any emotional data.

[2200] Input: Temporarily saved face photo data, personal information, emotional data

[2201] Output: Facial photo data, personal information, and emotional data sent to the server

[2202] Step 3:

[2203] The server stores the received facial photograph and emotion data in a database. After verifying that the facial photograph data and emotion data are valid, the server stores these data in the database.

[2204] Input: Transmitted face photo data, personal information, emotional data

[2205] Output: Facial photo data, personal information, and emotional data stored in a database

[2206] Step 4:

[2207] Once registration is complete, the server notifies the terminal, and the terminal displays a message to the user indicating that registration is complete.

[2208] Input: Facial photo data, personal information, and emotional data stored in the database

[2209] Output: Notification of successful registration, displayed message

[2210] Step 5:

[2211] The user or a third party sends an image generation request, which includes facial photo data and emotion data. The device temporarily stores this data and then sends a matching request to the server.

[2212] Input: Image generation request, facial photo data, emotion data

[2213] Output: Request data temporarily saved on the device, matching request sent

[2214] Step 6:

[2215] The server compares the facial photos in the database with the emotion data and notifies the device of the results. If the comparison results match, the server stops image generation and notifies the device of the reason. If the comparison results do not match, the server allows image generation.

[2216] Input: Matching request sent, face photo data in the database, emotion data

[2217] Output: Matching result, notification of abort if matched, permission to generate if not matched

[2218] Step 7:

[2219] After generation, the regenerated image is sent to the server and re-matched using a facial recognition algorithm and emotional data.

[2220] Input: Generated image data

[2221] Output: Generated image data sent

[2222] Step 8:

[2223] The server compares the generated image data with the database, and if the matching facial photo or emotional state matches the registered data, the generated image is invalidated. The result is notified to the terminal. If there is no match, the generated image is returned to the user.

[2224] Input: Generated image data, face photo data in the database, emotion data

[2225] Output: Matching result, invalidation notification if matched, image returned if not matched

[2226] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2227] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2228] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2229] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2230] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2231] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2232] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2233] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2234] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2235] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2236] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2237] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2238] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2239] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2240] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2241] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2242] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2243] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2244] Furthermore, the ha...

Claims

1. A system for preventing a specific individual from misusing their own facial photograph, A means of registering a face photo, A means for storing the registered facial photograph in a database; a means for matching against a database when requesting image generation; means for permitting or denying image generation based on the result of the matching; The system includes a means for matching the generated image again with the database.

2. The system of claim 1 , further comprising means for verifying the suitability of an uploaded facial photograph using a facial recognition algorithm.

3. The system of claim 1 further comprising means for disabling image generation and providing notification if a match is found with a blacklist of facial photographs.

Images